ollama - 💡(How to fix) Fix [Windows] CUDA error: out of memory (cuMemAddressReserve) on 8x GPU setup [1 comments, 1 participants]
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shankangkeError Message
PS C:\Users\its> ollama serve
time=2026-03-24T11:31:02.892+08:00 level=INFO source=routes.go:1727 msg="server config" env="map[CUDA_VISIBLE_DEVICES:0, 1, 2, 3, 4, 5, 6, 7 GGML_VK_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:0 OLLAMA_DEBUG:INFO OLLAMA_EDITOR: OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_KEEP_ALIVE:2562047h47m16.854775807s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:C:\Users\its\.ollama\models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NO_CLOUD:false OLLAMA_NUM_PARALLEL:8 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_REMOTES:[ollama.com] OLLAMA_SCHED_SPREAD:true OLLAMA_VULKAN:false ROCR_VISIBLE_DEVICES:]"
time=2026-03-24T11:31:02.922+08:00 level=INFO source=routes.go:1729 msg="Ollama cloud disabled: false"
time=2026-03-24T11:31:02.934+08:00 level=INFO source=images.go:477 msg="total blobs: 25"
time=2026-03-24T11:31:02.939+08:00 level=INFO source=images.go:484 msg="total unused blobs removed: 0"
time=2026-03-24T11:31:02.942+08:00 level=INFO source=routes.go:1782 msg="Listening on [::]:11434 (version 0.18.2)"
time=2026-03-24T11:31:02.944+08:00 level=INFO source=runner.go:67 msg="discovering available GPUs..."
time=2026-03-24T11:31:02.981+08:00 level=WARN source=runner.go:485 msg="user overrode visible devices" CUDA_VISIBLE_DEVICES="0, 1, 2, 3, 4, 5, 6, 7"
time=2026-03-24T11:31:02.981+08:00 level=WARN source=runner.go:489 msg="if GPUs are not correctly discovered, unset and try again"
time=2026-03-24T11:31:03.001+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58225"
time=2026-03-24T11:31:05.977+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58369"
time=2026-03-24T11:31:08.823+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58526"
time=2026-03-24T11:31:11.200+08:00 level=INFO source=runner.go:106 msg="experimental Vulkan support disabled. To enable, set OLLAMA_VULKAN=1"
time=2026-03-24T11:31:11.205+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58761"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58763"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58762"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58764"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58767"
time=2026-03-24T11:31:11.208+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58765"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58766"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58769"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58768"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58771"
time=2026-03-24T11:31:11.210+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58770"
time=2026-03-24T11:31:11.210+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58772"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58773"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58774"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58775"
time=2026-03-24T11:31:11.212+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 58776"
time=2026-03-24T11:31:14.490+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 filter_id="" library=CUDA compute=7.5 name=CUDA0 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:04:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.490+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 filter_id="" library=CUDA compute=7.5 name=CUDA1 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:05:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-8c020d1f-280d-e705-8f69-3a5342688f1a filter_id="" library=CUDA compute=7.5 name=CUDA2 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:08:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-36588785-9363-4c15-053d-05548b16e1a1 filter_id="" library=CUDA compute=7.5 name=CUDA4 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:84:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 filter_id="" library=CUDA compute=7.5 name=CUDA5 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:85:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e filter_id="" library=CUDA compute=7.5 name=CUDA7 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:89:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e filter_id="" library=CUDA compute=7.5 name=CUDA6 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:88:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 filter_id="" library=CUDA compute=7.5 name=CUDA3 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:09:00.0 type=discrete total="24.0 GiB" available="23.2 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=routes.go:1832 msg="vram-based default context" total_vram="192.0 GiB" default_num_ctx=262144
[GIN] 2026/03/24 - 11:31:14 | 200 | 0s | 127.0.0.1 | HEAD "/"
[GIN] 2026/03/24 - 11:31:14 | 200 | 345.3334ms | 127.0.0.1 | POST "/api/show"
[GIN] 2026/03/24 - 11:31:15 | 200 | 333.648ms | 127.0.0.1 | POST "/api/show"
time=2026-03-24T11:31:15.588+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --ollama-engine --port 62523"
time=2026-03-24T11:31:18.579+08:00 level=INFO source=runner.go:464 msg="failure during GPU discovery" OLLAMA_LIBRARY_PATH="[C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama\cuda_v13]" extra_envs=map[] error="failed to finish discovery before timeout"
time=2026-03-24T11:31:18.581+08:00 level=WARN source=runner.go:356 msg="unable to refresh free memory, using old values"
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:148 msg=packages count=2
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:195 msg="" package=0 cores=14 efficiency=0 threads=28
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:195 msg="" package=1 cores=14 efficiency=0 threads=28
llama_model_loader: loaded meta data with 53 key-value pairs and 809 tensors from C:\Users\its.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = minimax-m2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 40
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Minimax-M2.5
llama_model_loader: - kv 6: general.basename str = Minimax-M2.5
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 256x4.9B
llama_model_loader: - kv 9: general.license str = other
llama_model_loader: - kv 10: general.license.name str = modified-mit
llama_model_loader: - kv 11: general.license.link str = https://github.com/MiniMax-AI/MiniMax...
llama_model_loader: - kv 12: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 13: general.base_model.count u32 = 1
llama_model_loader: - kv 14: general.base_model.0.name str = MiniMax M2.5
llama_model_loader: - kv 15: general.base_model.0.organization str = MiniMaxAI
llama_model_loader: - kv 16: general.base_model.0.repo_url str = https://huggingface.co/MiniMaxAI/Mini...
llama_model_loader: - kv 17: general.tags arr[str,2] = ["unsloth", "text-generation"]
llama_model_loader: - kv 18: minimax-m2.block_count u32 = 62
llama_model_loader: - kv 19: minimax-m2.context_length u32 = 196608
llama_model_loader: - kv 20: minimax-m2.embedding_length u32 = 3072
llama_model_loader: - kv 21: minimax-m2.feed_forward_length u32 = 1536
llama_model_loader: - kv 22: minimax-m2.attention.head_count u32 = 48
llama_model_loader: - kv 23: minimax-m2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 24: minimax-m2.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 25: minimax-m2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: minimax-m2.expert_count u32 = 256
llama_model_loader: - kv 27: minimax-m2.expert_used_count u32 = 8
llama_model_loader: - kv 28: minimax-m2.expert_gating_func u32 = 2
llama_model_loader: - kv 29: minimax-m2.attention.key_length u32 = 128
llama_model_loader: - kv 30: minimax-m2.attention.value_length u32 = 128
llama_model_loader: - kv 31: minimax-m2.expert_feed_forward_length u32 = 1536
llama_model_loader: - kv 32: minimax-m2.rope.dimension_count u32 = 64
llama_model_loader: - kv 33: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 34: tokenizer.ggml.pre str = minimax-m2
llama_model_loader: - kv 35: tokenizer.ggml.tokens arr[str,200064] = ["Ā", "ā", "Ă", "ă", "Ą", "ą", ...
llama_model_loader: - kv 36: tokenizer.ggml.token_type arr[i32,200064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 37: tokenizer.ggml.merges arr[str,199744] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "e r...
llama_model_loader: - kv 38: tokenizer.ggml.bos_token_id u32 = 200034
llama_model_loader: - kv 39: tokenizer.ggml.eos_token_id u32 = 200020
llama_model_loader: - kv 40: tokenizer.ggml.unknown_token_id u32 = 200021
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 200004
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.chat_template str = {# Unsloth template fixes #}\n{# -----...
llama_model_loader: - kv 44: general.quantization_version u32 = 2
llama_model_loader: - kv 45: general.file_type u32 = 12
llama_model_loader: - kv 46: quantize.imatrix.file str = MiniMax-M2.5-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 47: quantize.imatrix.dataset str = unsloth_calibration_MiniMax-M2.5.txt
llama_model_loader: - kv 48: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 49: quantize.imatrix.chunks_count u32 = 81
llama_model_loader: - kv 50: split.no u16 = 0
llama_model_loader: - kv 51: split.tensors.count i32 = 809
llama_model_loader: - kv 52: split.count u16 = 0
llama_model_loader: - type f32: 373 tensors
llama_model_loader: - type q3_K: 173 tensors
llama_model_loader: - type q4_K: 232 tensors
llama_model_loader: - type q5_K: 20 tensors
llama_model_loader: - type q6_K: 11 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q3_K - Medium
print_info: file size = 94.33 GiB (3.54 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load: - 200004 ('<fim_pad>')
load: - 200005 ('<reponame>')
load: - 200020 ('[e~[')
load: special tokens cache size = 54
load: token to piece cache size = 1.3355 MB
print_info: arch = minimax-m2
print_info: vocab_only = 1
print_info: no_alloc = 0
print_info: model type = ?B
print_info: model params = 228.69 B
print_info: general.name = Minimax-M2.5
print_info: vocab type = BPE
print_info: n_vocab = 200064
print_info: n_merges = 199744
print_info: BOS token = 200034 ']!b['
print_info: EOS token = 200020 '[e['
print_info: UNK token = 200021 ']!d~['
print_info: PAD token = 200004 '<fim_pad>'
print_info: LF token = 10 'Ċ'
print_info: FIM PRE token = 200001 '<fim_prefix>'
print_info: FIM SUF token = 200003 '<fim_suffix>'
print_info: FIM MID token = 200002 '<fim_middle>'
print_info: FIM PAD token = 200004 '<fim_pad>'
print_info: FIM REP token = 200005 '<reponame>'
print_info: EOG token = 200004 '<fim_pad>'
print_info: EOG token = 200005 '<reponame>'
print_info: EOG token = 200020 '[e~['
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2026-03-24T11:31:19.413+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\Users\its\AppData\Local\Programs\Ollama\ollama.exe runner --model C:\Users\its\.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf --port 62697"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:484 msg="system memory" total="255.9 GiB" free="234.9 GiB" free_swap="238.6 GiB"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-8c020d1f-280d-e705-8f69-3a5342688f1a library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 library=CUDA available="22.7 GiB" free="23.2 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-36588785-9363-4c15-053d-05548b16e1a1 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e library=CUDA available="22.9 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=server.go:497 msg="loading model" "model layers"=63 requested=-1
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA0 size="10.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA1 size="11.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA2 size="11.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA3 size="11.3 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA4 size="12.3 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA5 size="12.0 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA6 size="12.0 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA7 size="12.1 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA0 size="224.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA1 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA2 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA3 size="224.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA4 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA5 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA6 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA7 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA0 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA1 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA2 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA3 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA4 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA5 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA6 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA7 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:272 msg="total memory" size="111.4 GiB"
time=2026-03-24T11:31:20.810+08:00 level=INFO source=runner.go:965 msg="starting go runner"
load_backend: loaded CPU backend from C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-haswell.dll
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 8 CUDA devices:
Device 0: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36
Device 1: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-f7ad384d-30ee-b723-a586-06a5b29b8900
Device 2: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-8c020d1f-280d-e705-8f69-3a5342688f1a
Device 3: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538
Device 4: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-36588785-9363-4c15-053d-05548b16e1a1
Device 5: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0
Device 6: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e
Device 7: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e
load_backend: loaded CUDA backend from C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama\cuda_v13\ggml-cuda.dll
time=2026-03-24T11:31:21.082+08:00 level=INFO source=ggml.go:104 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 CUDA.1.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.1.USE_GRAPHS=1 CUDA.1.PEER_MAX_BATCH_SIZE=128 CUDA.2.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.2.USE_GRAPHS=1 CUDA.2.PEER_MAX_BATCH_SIZE=128 CUDA.3.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.3.USE_GRAPHS=1 CUDA.3.PEER_MAX_BATCH_SIZE=128 CUDA.4.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.4.USE_GRAPHS=1 CUDA.4.PEER_MAX_BATCH_SIZE=128 CUDA.5.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.5.USE_GRAPHS=1 CUDA.5.PEER_MAX_BATCH_SIZE=128 CUDA.6.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.6.USE_GRAPHS=1 CUDA.6.PEER_MAX_BATCH_SIZE=128 CUDA.7.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.7.USE_GRAPHS=1 CUDA.7.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang)
time=2026-03-24T11:31:21.085+08:00 level=INFO source=runner.go:1001 msg="Server listening on 127.0.0.1:62697"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=runner.go:895 msg=load request="{Operation:commit LoraPath:[] Parallel:8 BatchSize:512 FlashAttention:Auto KvSize:8192 KvCacheType: NumThreads:28 GPULayers:63[ID:GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 Layers:7(0..6) ID:GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 Layers:8(7..14) ID:GPU-8c020d1f-280d-e705-8f69-3a5342688f1a Layers:8(15..22) ID:GPU-36588785-9363-4c15-053d-05548b16e1a1 Layers:8(23..30) ID:GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 Layers:8(31..38) ID:GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e Layers:8(39..46) ID:GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e Layers:8(47..54) ID:GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 Layers:8(55..62)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=server.go:1350 msg="waiting for llama runner to start responding"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server loading model"
ggml_backend_cuda_device_get_memory device GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA0 (Quadro RTX 6000) (0000:04:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA1 (Quadro RTX 6000) (0000:05:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-8c020d1f-280d-e705-8f69-3a5342688f1a utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA2 (Quadro RTX 6000) (0000:08:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-36588785-9363-4c15-053d-05548b16e1a1 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA4 (Quadro RTX 6000) (0000:84:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA5 (Quadro RTX 6000) (0000:85:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA7 (Quadro RTX 6000) (0000:89:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e utilizing NVML memory reporting free: 24959668224 total: 25769803776
llama_model_load_from_file_impl: using device CUDA6 (Quadro RTX 6000) (0000:88:00.0) - 23803 MiB free
ggml_backend_cuda_device_get_memory device GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 utilizing NVML memory reporting free: 24738156544 total: 25769803776
llama_model_load_from_file_impl: using device CUDA3 (Quadro RTX 6000) (0000:09:00.0) - 23592 MiB free
llama_model_loader: loaded meta data with 53 key-value pairs and 809 tensors from C:\Users\its.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = minimax-m2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 40
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Minimax-M2.5
llama_model_loader: - kv 6: general.basename str = Minimax-M2.5
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 256x4.9B
llama_model_loader: - kv 9: general.license str = other
llama_model_loader: - kv 10: general.license.name str = modified-mit
llama_model_loader: - kv 11: general.license.link str = https://github.com/MiniMax-AI/MiniMax...
llama_model_loader: - kv 12: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 13: general.base_model.count u32 = 1
llama_model_loader: - kv 14: general.base_model.0.name str = MiniMax M2.5
llama_model_loader: - kv 15: general.base_model.0.organization str = MiniMaxAI
llama_model_loader: - kv 16: general.base_model.0.repo_url str = https://huggingface.co/MiniMaxAI/Mini...
llama_model_loader: - kv 17: general.tags arr[str,2] = ["unsloth", "text-generation"]
llama_model_loader: - kv 18: minimax-m2.block_count u32 = 62
llama_model_loader: - kv 19: minimax-m2.context_length u32 = 196608
llama_model_loader: - kv 20: minimax-m2.embedding_length u32 = 3072
llama_model_loader: - kv 21: minimax-m2.feed_forward_length u32 = 1536
llama_model_loader: - kv 22: minimax-m2.attention.head_count u32 = 48
llama_model_loader: - kv 23: minimax-m2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 24: minimax-m2.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 25: minimax-m2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: minimax-m2.expert_count u32 = 256
llama_model_loader: - kv 27: minimax-m2.expert_used_count u32 = 8
llama_model_loader: - kv 28: minimax-m2.expert_gating_func u32 = 2
llama_model_loader: - kv 29: minimax-m2.attention.key_length u32 = 128
llama_model_loader: - kv 30: minimax-m2.attention.value_length u32 = 128
llama_model_loader: - kv 31: minimax-m2.expert_feed_forward_length u32 = 1536
llama_model_loader: - kv 32: minimax-m2.rope.dimension_count u32 = 64
llama_model_loader: - kv 33: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 34: tokenizer.ggml.pre str = minimax-m2
llama_model_loader: - kv 35: tokenizer.ggml.tokens arr[str,200064] = ["Ā", "ā", "Ă", "ă", "Ą", "ą", ...
llama_model_loader: - kv 36: tokenizer.ggml.token_type arr[i32,200064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 37: tokenizer.ggml.merges arr[str,199744] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "e r...
llama_model_loader: - kv 38: tokenizer.ggml.bos_token_id u32 = 200034
llama_model_loader: - kv 39: tokenizer.ggml.eos_token_id u32 = 200020
llama_model_loader: - kv 40: tokenizer.ggml.unknown_token_id u32 = 200021
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 200004
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.chat_template str = {# Unsloth template fixes #}\n{# -----...
llama_model_loader: - kv 44: general.quantization_version u32 = 2
llama_model_loader: - kv 45: general.file_type u32 = 12
llama_model_loader: - kv 46: quantize.imatrix.file str = MiniMax-M2.5-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 47: quantize.imatrix.dataset str = unsloth_calibration_MiniMax-M2.5.txt
llama_model_loader: - kv 48: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 49: quantize.imatrix.chunks_count u32 = 81
llama_model_loader: - kv 50: split.no u16 = 0
llama_model_loader: - kv 51: split.tensors.count i32 = 809
llama_model_loader: - kv 52: split.count u16 = 0
llama_model_loader: - type f32: 373 tensors
llama_model_loader: - type q3_K: 173 tensors
llama_model_loader: - type q4_K: 232 tensors
llama_model_loader: - type q5_K: 20 tensors
llama_model_loader: - type q6_K: 11 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q3_K - Medium
print_info: file size = 94.33 GiB (3.54 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load: - 200004 ('<fim_pad>')
load: - 200005 ('<reponame>')
load: - 200020 ('[e~[')
load: special tokens cache size = 54
load: token to piece cache size = 1.3355 MB
print_info: arch = minimax-m2
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 196608
print_info: n_embd = 3072
print_info: n_embd_inp = 3072
print_info: n_layer = 62
print_info: n_head = 48
print_info: n_head_kv = 8
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 6
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 1536
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 196608
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 230B.A10B
print_info: model params = 228.69 B
print_info: general.name = Minimax-M2.5
print_info: vocab type = BPE
print_info: n_vocab = 200064
print_info: n_merges = 199744
print_info: BOS token = 200034 ']!b['
print_info: EOS token = 200020 '[e['
print_info: UNK token = 200021 ']!d~['
print_info: PAD token = 200004 '<fim_pad>'
print_info: LF token = 10 'Ċ'
print_info: FIM PRE token = 200001 '<fim_prefix>'
print_info: FIM SUF token = 200003 '<fim_suffix>'
print_info: FIM MID token = 200002 '<fim_middle>'
print_info: FIM PAD token = 200004 '<fim_pad>'
print_info: FIM REP token = 200005 '<reponame>'
print_info: EOG token = 200004 '<fim_pad>'
print_info: EOG token = 200005 '<reponame>'
print_info: EOG token = 200020 '[e~['
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading 62 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 63/63 layers to GPU
load_tensors: CPU model buffer size = 329.70 MiB
load_tensors: CUDA0 model buffer size = 11054.66 MiB
load_tensors: CUDA1 model buffer size = 12107.34 MiB
load_tensors: CUDA2 model buffer size = 12093.41 MiB
load_tensors: CUDA3 model buffer size = 11536.70 MiB
load_tensors: CUDA4 model buffer size = 12552.41 MiB
load_tensors: CUDA5 model buffer size = 12251.66 MiB
load_tensors: CUDA6 model buffer size = 12260.34 MiB
load_tensors: CUDA7 model buffer size = 12409.91 MiB
llama_context: constructing llama_context
llama_context: n_seq_max = 8
llama_context: n_ctx = 8192
llama_context: n_ctx_seq = 1024
llama_context: n_batch = 4096
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 5000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (1024) < n_ctx_train (196608) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 6.20 MiB
llama_kv_cache: CUDA0 KV buffer size = 224.00 MiB
llama_kv_cache: CUDA1 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA2 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA3 KV buffer size = 224.00 MiB
llama_kv_cache: CUDA4 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA5 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA6 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA7 KV buffer size = 256.00 MiB
llama_kv_cache: size = 1984.00 MiB ( 1024 cells, 62 layers, 8/8 seqs), K (f16): 992.00 MiB, V (f16): 992.00 MiB
llama_context: pipeline parallelism enabled (n_copies=4)
llama_context: Flash Attention was auto, set to enabled
CUDA error: out of memory
current device: 6, in function alloc at C:\a\ollama\ollama\ml\backend\ggml\ggml\src\ggml-cuda\ggml-cuda.cu:576
cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)
C:\a\ollama\ollama\ml\backend\ggml\ggml\src\ggml-cuda\ggml-cuda.cu:94: CUDA error
time=2026-03-24T11:32:15.465+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server not responding"
time=2026-03-24T11:32:17.522+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server error"
time=2026-03-24T11:32:17.615+08:00 level=ERROR source=server.go:303 msg="llama runner terminated" error="exit status 1"
time=2026-03-24T11:32:17.772+08:00 level=INFO source=sched.go:511 msg="Load failed" model=C:\Users\its.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf error="llama runner process has terminated: CUDA error"
[GIN] 2026/03/24 - 11:32:17 | 500 | 1m2s | 127.0.0.1 | POST "/api/generate"
Root Cause
The error specifically occurs at cuMemAddressReserve on a random device. The physical VRAM is more than sufficient for this model (each card has 24GB, 8 cards total). The crash is clearly not caused by a lack of physical VRAM.
Code Example
FROM ./MiniMax-M2.5-UD-Q3_K_XL.gguf
PARAMETER num_ctx 1024
---
PS C:\Users\its> ollama serve
time=2026-03-24T11:31:02.892+08:00 level=INFO source=routes.go:1727 msg="server config" env="map[CUDA_VISIBLE_DEVICES:0, 1, 2, 3, 4, 5, 6, 7 GGML_VK_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:0 OLLAMA_DEBUG:INFO OLLAMA_EDITOR: OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_KEEP_ALIVE:2562047h47m16.854775807s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:C:\\Users\\its\\.ollama\\models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NO_CLOUD:false OLLAMA_NUM_PARALLEL:8 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_REMOTES:[ollama.com] OLLAMA_SCHED_SPREAD:true OLLAMA_VULKAN:false ROCR_VISIBLE_DEVICES:]"
time=2026-03-24T11:31:02.922+08:00 level=INFO source=routes.go:1729 msg="Ollama cloud disabled: false"
time=2026-03-24T11:31:02.934+08:00 level=INFO source=images.go:477 msg="total blobs: 25"
time=2026-03-24T11:31:02.939+08:00 level=INFO source=images.go:484 msg="total unused blobs removed: 0"
time=2026-03-24T11:31:02.942+08:00 level=INFO source=routes.go:1782 msg="Listening on [::]:11434 (version 0.18.2)"
time=2026-03-24T11:31:02.944+08:00 level=INFO source=runner.go:67 msg="discovering available GPUs..."
time=2026-03-24T11:31:02.981+08:00 level=WARN source=runner.go:485 msg="user overrode visible devices" CUDA_VISIBLE_DEVICES="0, 1, 2, 3, 4, 5, 6, 7"
time=2026-03-24T11:31:02.981+08:00 level=WARN source=runner.go:489 msg="if GPUs are not correctly discovered, unset and try again"
time=2026-03-24T11:31:03.001+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58225"
time=2026-03-24T11:31:05.977+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58369"
time=2026-03-24T11:31:08.823+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58526"
time=2026-03-24T11:31:11.200+08:00 level=INFO source=runner.go:106 msg="experimental Vulkan support disabled. To enable, set OLLAMA_VULKAN=1"
time=2026-03-24T11:31:11.205+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58761"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58763"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58762"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58764"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58767"
time=2026-03-24T11:31:11.208+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58765"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58766"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58769"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58768"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58771"
time=2026-03-24T11:31:11.210+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58770"
time=2026-03-24T11:31:11.210+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58772"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58773"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58774"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58775"
time=2026-03-24T11:31:11.212+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58776"
time=2026-03-24T11:31:14.490+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 filter_id="" library=CUDA compute=7.5 name=CUDA0 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:04:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.490+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 filter_id="" library=CUDA compute=7.5 name=CUDA1 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:05:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-8c020d1f-280d-e705-8f69-3a5342688f1a filter_id="" library=CUDA compute=7.5 name=CUDA2 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:08:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-36588785-9363-4c15-053d-05548b16e1a1 filter_id="" library=CUDA compute=7.5 name=CUDA4 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:84:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 filter_id="" library=CUDA compute=7.5 name=CUDA5 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:85:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e filter_id="" library=CUDA compute=7.5 name=CUDA7 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:89:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e filter_id="" library=CUDA compute=7.5 name=CUDA6 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:88:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 filter_id="" library=CUDA compute=7.5 name=CUDA3 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:09:00.0 type=discrete total="24.0 GiB" available="23.2 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=routes.go:1832 msg="vram-based default context" total_vram="192.0 GiB" default_num_ctx=262144
[GIN] 2026/03/24 - 11:31:14 | 200 | 0s | 127.0.0.1 | HEAD "/"
[GIN] 2026/03/24 - 11:31:14 | 200 | 345.3334ms | 127.0.0.1 | POST "/api/show"
[GIN] 2026/03/24 - 11:31:15 | 200 | 333.648ms | 127.0.0.1 | POST "/api/show"
time=2026-03-24T11:31:15.588+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 62523"
time=2026-03-24T11:31:18.579+08:00 level=INFO source=runner.go:464 msg="failure during GPU discovery" OLLAMA_LIBRARY_PATH="[C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\lib\\ollama C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\lib\\ollama\\cuda_v13]" extra_envs=map[] error="failed to finish discovery before timeout"
time=2026-03-24T11:31:18.581+08:00 level=WARN source=runner.go:356 msg="unable to refresh free memory, using old values"
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:148 msg=packages count=2
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:195 msg="" package=0 cores=14 efficiency=0 threads=28
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:195 msg="" package=1 cores=14 efficiency=0 threads=28
llama_model_loader: loaded meta data with 53 key-value pairs and 809 tensors from C:\Users\its\.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = minimax-m2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 40
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Minimax-M2.5
llama_model_loader: - kv 6: general.basename str = Minimax-M2.5
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 256x4.9B
llama_model_loader: - kv 9: general.license str = other
llama_model_loader: - kv 10: general.license.name str = modified-mit
llama_model_loader: - kv 11: general.license.link str = https://github.com/MiniMax-AI/MiniMax...
llama_model_loader: - kv 12: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 13: general.base_model.count u32 = 1
llama_model_loader: - kv 14: general.base_model.0.name str = MiniMax M2.5
llama_model_loader: - kv 15: general.base_model.0.organization str = MiniMaxAI
llama_model_loader: - kv 16: general.base_model.0.repo_url str = https://huggingface.co/MiniMaxAI/Mini...
llama_model_loader: - kv 17: general.tags arr[str,2] = ["unsloth", "text-generation"]
llama_model_loader: - kv 18: minimax-m2.block_count u32 = 62
llama_model_loader: - kv 19: minimax-m2.context_length u32 = 196608
llama_model_loader: - kv 20: minimax-m2.embedding_length u32 = 3072
llama_model_loader: - kv 21: minimax-m2.feed_forward_length u32 = 1536
llama_model_loader: - kv 22: minimax-m2.attention.head_count u32 = 48
llama_model_loader: - kv 23: minimax-m2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 24: minimax-m2.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 25: minimax-m2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: minimax-m2.expert_count u32 = 256
llama_model_loader: - kv 27: minimax-m2.expert_used_count u32 = 8
llama_model_loader: - kv 28: minimax-m2.expert_gating_func u32 = 2
llama_model_loader: - kv 29: minimax-m2.attention.key_length u32 = 128
llama_model_loader: - kv 30: minimax-m2.attention.value_length u32 = 128
llama_model_loader: - kv 31: minimax-m2.expert_feed_forward_length u32 = 1536
llama_model_loader: - kv 32: minimax-m2.rope.dimension_count u32 = 64
llama_model_loader: - kv 33: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 34: tokenizer.ggml.pre str = minimax-m2
llama_model_loader: - kv 35: tokenizer.ggml.tokens arr[str,200064] = ["Ā", "ā", "Ă", "ă", "Ą", "ą", ...
llama_model_loader: - kv 36: tokenizer.ggml.token_type arr[i32,200064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 37: tokenizer.ggml.merges arr[str,199744] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "e r...
llama_model_loader: - kv 38: tokenizer.ggml.bos_token_id u32 = 200034
llama_model_loader: - kv 39: tokenizer.ggml.eos_token_id u32 = 200020
llama_model_loader: - kv 40: tokenizer.ggml.unknown_token_id u32 = 200021
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 200004
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.chat_template str = {# Unsloth template fixes #}\n{# -----...
llama_model_loader: - kv 44: general.quantization_version u32 = 2
llama_model_loader: - kv 45: general.file_type u32 = 12
llama_model_loader: - kv 46: quantize.imatrix.file str = MiniMax-M2.5-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 47: quantize.imatrix.dataset str = unsloth_calibration_MiniMax-M2.5.txt
llama_model_loader: - kv 48: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 49: quantize.imatrix.chunks_count u32 = 81
llama_model_loader: - kv 50: split.no u16 = 0
llama_model_loader: - kv 51: split.tensors.count i32 = 809
llama_model_loader: - kv 52: split.count u16 = 0
llama_model_loader: - type f32: 373 tensors
llama_model_loader: - type q3_K: 173 tensors
llama_model_loader: - type q4_K: 232 tensors
llama_model_loader: - type q5_K: 20 tensors
llama_model_loader: - type q6_K: 11 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q3_K - Medium
print_info: file size = 94.33 GiB (3.54 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load: - 200004 ('<fim_pad>')
load: - 200005 ('<reponame>')
load: - 200020 ('[e~[')
load: special tokens cache size = 54
load: token to piece cache size = 1.3355 MB
print_info: arch = minimax-m2
print_info: vocab_only = 1
print_info: no_alloc = 0
print_info: model type = ?B
print_info: model params = 228.69 B
print_info: general.name = Minimax-M2.5
print_info: vocab type = BPE
print_info: n_vocab = 200064
print_info: n_merges = 199744
print_info: BOS token = 200034 ']~!b['
print_info: EOS token = 200020 '[e~['
print_info: UNK token = 200021 ']!d~['
print_info: PAD token = 200004 '<fim_pad>'
print_info: LF token = 10 'Ċ'
print_info: FIM PRE token = 200001 '<fim_prefix>'
print_info: FIM SUF token = 200003 '<fim_suffix>'
print_info: FIM MID token = 200002 '<fim_middle>'
print_info: FIM PAD token = 200004 '<fim_pad>'
print_info: FIM REP token = 200005 '<reponame>'
print_info: EOG token = 200004 '<fim_pad>'
print_info: EOG token = 200005 '<reponame>'
print_info: EOG token = 200020 '[e~['
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2026-03-24T11:31:19.413+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model C:\\Users\\its\\.ollama\\models\\blobs\\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf --port 62697"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:484 msg="system memory" total="255.9 GiB" free="234.9 GiB" free_swap="238.6 GiB"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-8c020d1f-280d-e705-8f69-3a5342688f1a library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 library=CUDA available="22.7 GiB" free="23.2 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-36588785-9363-4c15-053d-05548b16e1a1 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e library=CUDA available="22.9 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=server.go:497 msg="loading model" "model layers"=63 requested=-1
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA0 size="10.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA1 size="11.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA2 size="11.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA3 size="11.3 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA4 size="12.3 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA5 size="12.0 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA6 size="12.0 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA7 size="12.1 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA0 size="224.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA1 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA2 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA3 size="224.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA4 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA5 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA6 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA7 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA0 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA1 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA2 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA3 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA4 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA5 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA6 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA7 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:272 msg="total memory" size="111.4 GiB"
time=2026-03-24T11:31:20.810+08:00 level=INFO source=runner.go:965 msg="starting go runner"
load_backend: loaded CPU backend from C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-haswell.dll
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 8 CUDA devices:
Device 0: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36
Device 1: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-f7ad384d-30ee-b723-a586-06a5b29b8900
Device 2: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-8c020d1f-280d-e705-8f69-3a5342688f1a
Device 3: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538
Device 4: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-36588785-9363-4c15-053d-05548b16e1a1
Device 5: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0
Device 6: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e
Device 7: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e
load_backend: loaded CUDA backend from C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama\cuda_v13\ggml-cuda.dll
time=2026-03-24T11:31:21.082+08:00 level=INFO source=ggml.go:104 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 CUDA.1.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.1.USE_GRAPHS=1 CUDA.1.PEER_MAX_BATCH_SIZE=128 CUDA.2.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.2.USE_GRAPHS=1 CUDA.2.PEER_MAX_BATCH_SIZE=128 CUDA.3.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.3.USE_GRAPHS=1 CUDA.3.PEER_MAX_BATCH_SIZE=128 CUDA.4.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.4.USE_GRAPHS=1 CUDA.4.PEER_MAX_BATCH_SIZE=128 CUDA.5.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.5.USE_GRAPHS=1 CUDA.5.PEER_MAX_BATCH_SIZE=128 CUDA.6.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.6.USE_GRAPHS=1 CUDA.6.PEER_MAX_BATCH_SIZE=128 CUDA.7.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.7.USE_GRAPHS=1 CUDA.7.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang)
time=2026-03-24T11:31:21.085+08:00 level=INFO source=runner.go:1001 msg="Server listening on 127.0.0.1:62697"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=runner.go:895 msg=load request="{Operation:commit LoraPath:[] Parallel:8 BatchSize:512 FlashAttention:Auto KvSize:8192 KvCacheType: NumThreads:28 GPULayers:63[ID:GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 Layers:7(0..6) ID:GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 Layers:8(7..14) ID:GPU-8c020d1f-280d-e705-8f69-3a5342688f1a Layers:8(15..22) ID:GPU-36588785-9363-4c15-053d-05548b16e1a1 Layers:8(23..30) ID:GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 Layers:8(31..38) ID:GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e Layers:8(39..46) ID:GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e Layers:8(47..54) ID:GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 Layers:8(55..62)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=server.go:1350 msg="waiting for llama runner to start responding"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server loading model"
ggml_backend_cuda_device_get_memory device GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA0 (Quadro RTX 6000) (0000:04:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA1 (Quadro RTX 6000) (0000:05:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-8c020d1f-280d-e705-8f69-3a5342688f1a utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA2 (Quadro RTX 6000) (0000:08:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-36588785-9363-4c15-053d-05548b16e1a1 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA4 (Quadro RTX 6000) (0000:84:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA5 (Quadro RTX 6000) (0000:85:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA7 (Quadro RTX 6000) (0000:89:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e utilizing NVML memory reporting free: 24959668224 total: 25769803776
llama_model_load_from_file_impl: using device CUDA6 (Quadro RTX 6000) (0000:88:00.0) - 23803 MiB free
ggml_backend_cuda_device_get_memory device GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 utilizing NVML memory reporting free: 24738156544 total: 25769803776
llama_model_load_from_file_impl: using device CUDA3 (Quadro RTX 6000) (0000:09:00.0) - 23592 MiB free
llama_model_loader: loaded meta data with 53 key-value pairs and 809 tensors from C:\Users\its\.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = minimax-m2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 40
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Minimax-M2.5
llama_model_loader: - kv 6: general.basename str = Minimax-M2.5
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 256x4.9B
llama_model_loader: - kv 9: general.license str = other
llama_model_loader: - kv 10: general.license.name str = modified-mit
llama_model_loader: - kv 11: general.license.link str = https://github.com/MiniMax-AI/MiniMax...
llama_model_loader: - kv 12: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 13: general.base_model.count u32 = 1
llama_model_loader: - kv 14: general.base_model.0.name str = MiniMax M2.5
llama_model_loader: - kv 15: general.base_model.0.organization str = MiniMaxAI
llama_model_loader: - kv 16: general.base_model.0.repo_url str = https://huggingface.co/MiniMaxAI/Mini...
llama_model_loader: - kv 17: general.tags arr[str,2] = ["unsloth", "text-generation"]
llama_model_loader: - kv 18: minimax-m2.block_count u32 = 62
llama_model_loader: - kv 19: minimax-m2.context_length u32 = 196608
llama_model_loader: - kv 20: minimax-m2.embedding_length u32 = 3072
llama_model_loader: - kv 21: minimax-m2.feed_forward_length u32 = 1536
llama_model_loader: - kv 22: minimax-m2.attention.head_count u32 = 48
llama_model_loader: - kv 23: minimax-m2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 24: minimax-m2.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 25: minimax-m2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: minimax-m2.expert_count u32 = 256
llama_model_loader: - kv 27: minimax-m2.expert_used_count u32 = 8
llama_model_loader: - kv 28: minimax-m2.expert_gating_func u32 = 2
llama_model_loader: - kv 29: minimax-m2.attention.key_length u32 = 128
llama_model_loader: - kv 30: minimax-m2.attention.value_length u32 = 128
llama_model_loader: - kv 31: minimax-m2.expert_feed_forward_length u32 = 1536
llama_model_loader: - kv 32: minimax-m2.rope.dimension_count u32 = 64
llama_model_loader: - kv 33: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 34: tokenizer.ggml.pre str = minimax-m2
llama_model_loader: - kv 35: tokenizer.ggml.tokens arr[str,200064] = ["Ā", "ā", "Ă", "ă", "Ą", "ą", ...
llama_model_loader: - kv 36: tokenizer.ggml.token_type arr[i32,200064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 37: tokenizer.ggml.merges arr[str,199744] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "e r...
llama_model_loader: - kv 38: tokenizer.ggml.bos_token_id u32 = 200034
llama_model_loader: - kv 39: tokenizer.ggml.eos_token_id u32 = 200020
llama_model_loader: - kv 40: tokenizer.ggml.unknown_token_id u32 = 200021
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 200004
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.chat_template str = {# Unsloth template fixes #}\n{# -----...
llama_model_loader: - kv 44: general.quantization_version u32 = 2
llama_model_loader: - kv 45: general.file_type u32 = 12
llama_model_loader: - kv 46: quantize.imatrix.file str = MiniMax-M2.5-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 47: quantize.imatrix.dataset str = unsloth_calibration_MiniMax-M2.5.txt
llama_model_loader: - kv 48: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 49: quantize.imatrix.chunks_count u32 = 81
llama_model_loader: - kv 50: split.no u16 = 0
llama_model_loader: - kv 51: split.tensors.count i32 = 809
llama_model_loader: - kv 52: split.count u16 = 0
llama_model_loader: - type f32: 373 tensors
llama_model_loader: - type q3_K: 173 tensors
llama_model_loader: - type q4_K: 232 tensors
llama_model_loader: - type q5_K: 20 tensors
llama_model_loader: - type q6_K: 11 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q3_K - Medium
print_info: file size = 94.33 GiB (3.54 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load: - 200004 ('<fim_pad>')
load: - 200005 ('<reponame>')
load: - 200020 ('[e~[')
load: special tokens cache size = 54
load: token to piece cache size = 1.3355 MB
print_info: arch = minimax-m2
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 196608
print_info: n_embd = 3072
print_info: n_embd_inp = 3072
print_info: n_layer = 62
print_info: n_head = 48
print_info: n_head_kv = 8
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 6
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 1536
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 196608
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 230B.A10B
print_info: model params = 228.69 B
print_info: general.name = Minimax-M2.5
print_info: vocab type = BPE
print_info: n_vocab = 200064
print_info: n_merges = 199744
print_info: BOS token = 200034 ']~!b['
print_info: EOS token = 200020 '[e~['
print_info: UNK token = 200021 ']!d~['
print_info: PAD token = 200004 '<fim_pad>'
print_info: LF token = 10 'Ċ'
print_info: FIM PRE token = 200001 '<fim_prefix>'
print_info: FIM SUF token = 200003 '<fim_suffix>'
print_info: FIM MID token = 200002 '<fim_middle>'
print_info: FIM PAD token = 200004 '<fim_pad>'
print_info: FIM REP token = 200005 '<reponame>'
print_info: EOG token = 200004 '<fim_pad>'
print_info: EOG token = 200005 '<reponame>'
print_info: EOG token = 200020 '[e~['
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading 62 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 63/63 layers to GPU
load_tensors: CPU model buffer size = 329.70 MiB
load_tensors: CUDA0 model buffer size = 11054.66 MiB
load_tensors: CUDA1 model buffer size = 12107.34 MiB
load_tensors: CUDA2 model buffer size = 12093.41 MiB
load_tensors: CUDA3 model buffer size = 11536.70 MiB
load_tensors: CUDA4 model buffer size = 12552.41 MiB
load_tensors: CUDA5 model buffer size = 12251.66 MiB
load_tensors: CUDA6 model buffer size = 12260.34 MiB
load_tensors: CUDA7 model buffer size = 12409.91 MiB
llama_context: constructing llama_context
llama_context: n_seq_max = 8
llama_context: n_ctx = 8192
llama_context: n_ctx_seq = 1024
llama_context: n_batch = 4096
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 5000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (1024) < n_ctx_train (196608) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 6.20 MiB
llama_kv_cache: CUDA0 KV buffer size = 224.00 MiB
llama_kv_cache: CUDA1 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA2 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA3 KV buffer size = 224.00 MiB
llama_kv_cache: CUDA4 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA5 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA6 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA7 KV buffer size = 256.00 MiB
llama_kv_cache: size = 1984.00 MiB ( 1024 cells, 62 layers, 8/8 seqs), K (f16): 992.00 MiB, V (f16): 992.00 MiB
llama_context: pipeline parallelism enabled (n_copies=4)
llama_context: Flash Attention was auto, set to enabled
CUDA error: out of memory
current device: 6, in function alloc at C:\a\ollama\ollama\ml\backend\ggml\ggml\src\ggml-cuda\ggml-cuda.cu:576
cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)
C:\a\ollama\ollama\ml\backend\ggml\ggml\src\ggml-cuda\ggml-cuda.cu:94: CUDA error
time=2026-03-24T11:32:15.465+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server not responding"
time=2026-03-24T11:32:17.522+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server error"
time=2026-03-24T11:32:17.615+08:00 level=ERROR source=server.go:303 msg="llama runner terminated" error="exit status 1"
time=2026-03-24T11:32:17.772+08:00 level=INFO source=sched.go:511 msg="Load failed" model=C:\Users\its\.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf error="llama runner process has terminated: CUDA error"
[GIN] 2026/03/24 - 11:32:17 | 500 | 1m2s | 127.0.0.1 | POST "/api/generate"RAW_BUFFERClick to expand / collapse
What is the issue?
When attempting to run a model (MiniMax-M2.5-UD-Q3_K_XL.gguf) on a Windows machine equipped with 8x NVIDIA Quadro RTX 6000 GPUs, Ollama crashes with a CUDA error: out of memory just after the loading phase.
The error specifically occurs at cuMemAddressReserve on a random device. The physical VRAM is more than sufficient for this model (each card has 24GB, 8 cards total). The crash is clearly not caused by a lack of physical VRAM.
Modelfile
FROM ./MiniMax-M2.5-UD-Q3_K_XL.gguf
PARAMETER num_ctx 1024Relevant log output
PS C:\Users\its> ollama serve
time=2026-03-24T11:31:02.892+08:00 level=INFO source=routes.go:1727 msg="server config" env="map[CUDA_VISIBLE_DEVICES:0, 1, 2, 3, 4, 5, 6, 7 GGML_VK_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: HTTPS_PROXY: HTTP_PROXY: NO_PROXY: OLLAMA_CONTEXT_LENGTH:0 OLLAMA_DEBUG:INFO OLLAMA_EDITOR: OLLAMA_FLASH_ATTENTION:false OLLAMA_GPU_OVERHEAD:0 OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_KEEP_ALIVE:2562047h47m16.854775807s OLLAMA_KV_CACHE_TYPE: OLLAMA_LLM_LIBRARY: OLLAMA_LOAD_TIMEOUT:5m0s OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:C:\\Users\\its\\.ollama\\models OLLAMA_MULTIUSER_CACHE:false OLLAMA_NEW_ENGINE:false OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NO_CLOUD:false OLLAMA_NUM_PARALLEL:8 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://* vscode-webview://* vscode-file://*] OLLAMA_REMOTES:[ollama.com] OLLAMA_SCHED_SPREAD:true OLLAMA_VULKAN:false ROCR_VISIBLE_DEVICES:]"
time=2026-03-24T11:31:02.922+08:00 level=INFO source=routes.go:1729 msg="Ollama cloud disabled: false"
time=2026-03-24T11:31:02.934+08:00 level=INFO source=images.go:477 msg="total blobs: 25"
time=2026-03-24T11:31:02.939+08:00 level=INFO source=images.go:484 msg="total unused blobs removed: 0"
time=2026-03-24T11:31:02.942+08:00 level=INFO source=routes.go:1782 msg="Listening on [::]:11434 (version 0.18.2)"
time=2026-03-24T11:31:02.944+08:00 level=INFO source=runner.go:67 msg="discovering available GPUs..."
time=2026-03-24T11:31:02.981+08:00 level=WARN source=runner.go:485 msg="user overrode visible devices" CUDA_VISIBLE_DEVICES="0, 1, 2, 3, 4, 5, 6, 7"
time=2026-03-24T11:31:02.981+08:00 level=WARN source=runner.go:489 msg="if GPUs are not correctly discovered, unset and try again"
time=2026-03-24T11:31:03.001+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58225"
time=2026-03-24T11:31:05.977+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58369"
time=2026-03-24T11:31:08.823+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58526"
time=2026-03-24T11:31:11.200+08:00 level=INFO source=runner.go:106 msg="experimental Vulkan support disabled. To enable, set OLLAMA_VULKAN=1"
time=2026-03-24T11:31:11.205+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58761"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58763"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58762"
time=2026-03-24T11:31:11.207+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58764"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58767"
time=2026-03-24T11:31:11.208+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58765"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58766"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58769"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58768"
time=2026-03-24T11:31:11.209+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58771"
time=2026-03-24T11:31:11.210+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58770"
time=2026-03-24T11:31:11.210+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58772"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58773"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58774"
time=2026-03-24T11:31:11.211+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58775"
time=2026-03-24T11:31:11.212+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 58776"
time=2026-03-24T11:31:14.490+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 filter_id="" library=CUDA compute=7.5 name=CUDA0 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:04:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.490+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 filter_id="" library=CUDA compute=7.5 name=CUDA1 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:05:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-8c020d1f-280d-e705-8f69-3a5342688f1a filter_id="" library=CUDA compute=7.5 name=CUDA2 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:08:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-36588785-9363-4c15-053d-05548b16e1a1 filter_id="" library=CUDA compute=7.5 name=CUDA4 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:84:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 filter_id="" library=CUDA compute=7.5 name=CUDA5 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:85:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e filter_id="" library=CUDA compute=7.5 name=CUDA7 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:89:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e filter_id="" library=CUDA compute=7.5 name=CUDA6 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:88:00.0 type=discrete total="24.0 GiB" available="23.4 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=types.go:42 msg="inference compute" id=GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 filter_id="" library=CUDA compute=7.5 name=CUDA3 description="Quadro RTX 6000" libdirs=ollama,cuda_v13 driver=13.2 pci_id=0000:09:00.0 type=discrete total="24.0 GiB" available="23.2 GiB"
time=2026-03-24T11:31:14.491+08:00 level=INFO source=routes.go:1832 msg="vram-based default context" total_vram="192.0 GiB" default_num_ctx=262144
[GIN] 2026/03/24 - 11:31:14 | 200 | 0s | 127.0.0.1 | HEAD "/"
[GIN] 2026/03/24 - 11:31:14 | 200 | 345.3334ms | 127.0.0.1 | POST "/api/show"
[GIN] 2026/03/24 - 11:31:15 | 200 | 333.648ms | 127.0.0.1 | POST "/api/show"
time=2026-03-24T11:31:15.588+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --ollama-engine --port 62523"
time=2026-03-24T11:31:18.579+08:00 level=INFO source=runner.go:464 msg="failure during GPU discovery" OLLAMA_LIBRARY_PATH="[C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\lib\\ollama C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\lib\\ollama\\cuda_v13]" extra_envs=map[] error="failed to finish discovery before timeout"
time=2026-03-24T11:31:18.581+08:00 level=WARN source=runner.go:356 msg="unable to refresh free memory, using old values"
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:148 msg=packages count=2
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:195 msg="" package=0 cores=14 efficiency=0 threads=28
time=2026-03-24T11:31:18.582+08:00 level=INFO source=cpu_windows.go:195 msg="" package=1 cores=14 efficiency=0 threads=28
llama_model_loader: loaded meta data with 53 key-value pairs and 809 tensors from C:\Users\its\.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = minimax-m2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 40
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Minimax-M2.5
llama_model_loader: - kv 6: general.basename str = Minimax-M2.5
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 256x4.9B
llama_model_loader: - kv 9: general.license str = other
llama_model_loader: - kv 10: general.license.name str = modified-mit
llama_model_loader: - kv 11: general.license.link str = https://github.com/MiniMax-AI/MiniMax...
llama_model_loader: - kv 12: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 13: general.base_model.count u32 = 1
llama_model_loader: - kv 14: general.base_model.0.name str = MiniMax M2.5
llama_model_loader: - kv 15: general.base_model.0.organization str = MiniMaxAI
llama_model_loader: - kv 16: general.base_model.0.repo_url str = https://huggingface.co/MiniMaxAI/Mini...
llama_model_loader: - kv 17: general.tags arr[str,2] = ["unsloth", "text-generation"]
llama_model_loader: - kv 18: minimax-m2.block_count u32 = 62
llama_model_loader: - kv 19: minimax-m2.context_length u32 = 196608
llama_model_loader: - kv 20: minimax-m2.embedding_length u32 = 3072
llama_model_loader: - kv 21: minimax-m2.feed_forward_length u32 = 1536
llama_model_loader: - kv 22: minimax-m2.attention.head_count u32 = 48
llama_model_loader: - kv 23: minimax-m2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 24: minimax-m2.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 25: minimax-m2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: minimax-m2.expert_count u32 = 256
llama_model_loader: - kv 27: minimax-m2.expert_used_count u32 = 8
llama_model_loader: - kv 28: minimax-m2.expert_gating_func u32 = 2
llama_model_loader: - kv 29: minimax-m2.attention.key_length u32 = 128
llama_model_loader: - kv 30: minimax-m2.attention.value_length u32 = 128
llama_model_loader: - kv 31: minimax-m2.expert_feed_forward_length u32 = 1536
llama_model_loader: - kv 32: minimax-m2.rope.dimension_count u32 = 64
llama_model_loader: - kv 33: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 34: tokenizer.ggml.pre str = minimax-m2
llama_model_loader: - kv 35: tokenizer.ggml.tokens arr[str,200064] = ["Ā", "ā", "Ă", "ă", "Ą", "ą", ...
llama_model_loader: - kv 36: tokenizer.ggml.token_type arr[i32,200064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 37: tokenizer.ggml.merges arr[str,199744] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "e r...
llama_model_loader: - kv 38: tokenizer.ggml.bos_token_id u32 = 200034
llama_model_loader: - kv 39: tokenizer.ggml.eos_token_id u32 = 200020
llama_model_loader: - kv 40: tokenizer.ggml.unknown_token_id u32 = 200021
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 200004
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.chat_template str = {# Unsloth template fixes #}\n{# -----...
llama_model_loader: - kv 44: general.quantization_version u32 = 2
llama_model_loader: - kv 45: general.file_type u32 = 12
llama_model_loader: - kv 46: quantize.imatrix.file str = MiniMax-M2.5-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 47: quantize.imatrix.dataset str = unsloth_calibration_MiniMax-M2.5.txt
llama_model_loader: - kv 48: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 49: quantize.imatrix.chunks_count u32 = 81
llama_model_loader: - kv 50: split.no u16 = 0
llama_model_loader: - kv 51: split.tensors.count i32 = 809
llama_model_loader: - kv 52: split.count u16 = 0
llama_model_loader: - type f32: 373 tensors
llama_model_loader: - type q3_K: 173 tensors
llama_model_loader: - type q4_K: 232 tensors
llama_model_loader: - type q5_K: 20 tensors
llama_model_loader: - type q6_K: 11 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q3_K - Medium
print_info: file size = 94.33 GiB (3.54 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load: - 200004 ('<fim_pad>')
load: - 200005 ('<reponame>')
load: - 200020 ('[e~[')
load: special tokens cache size = 54
load: token to piece cache size = 1.3355 MB
print_info: arch = minimax-m2
print_info: vocab_only = 1
print_info: no_alloc = 0
print_info: model type = ?B
print_info: model params = 228.69 B
print_info: general.name = Minimax-M2.5
print_info: vocab type = BPE
print_info: n_vocab = 200064
print_info: n_merges = 199744
print_info: BOS token = 200034 ']~!b['
print_info: EOS token = 200020 '[e~['
print_info: UNK token = 200021 ']!d~['
print_info: PAD token = 200004 '<fim_pad>'
print_info: LF token = 10 'Ċ'
print_info: FIM PRE token = 200001 '<fim_prefix>'
print_info: FIM SUF token = 200003 '<fim_suffix>'
print_info: FIM MID token = 200002 '<fim_middle>'
print_info: FIM PAD token = 200004 '<fim_pad>'
print_info: FIM REP token = 200005 '<reponame>'
print_info: EOG token = 200004 '<fim_pad>'
print_info: EOG token = 200005 '<reponame>'
print_info: EOG token = 200020 '[e~['
print_info: max token length = 256
llama_model_load: vocab only - skipping tensors
time=2026-03-24T11:31:19.413+08:00 level=INFO source=server.go:430 msg="starting runner" cmd="C:\\Users\\its\\AppData\\Local\\Programs\\Ollama\\ollama.exe runner --model C:\\Users\\its\\.ollama\\models\\blobs\\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf --port 62697"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:484 msg="system memory" total="255.9 GiB" free="234.9 GiB" free_swap="238.6 GiB"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.451+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-8c020d1f-280d-e705-8f69-3a5342688f1a library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 library=CUDA available="22.7 GiB" free="23.2 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-36588785-9363-4c15-053d-05548b16e1a1 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e library=CUDA available="22.9 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=sched.go:491 msg="gpu memory" id=GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e library=CUDA available="23.0 GiB" free="23.4 GiB" minimum="457.0 MiB" overhead="0 B"
time=2026-03-24T11:31:19.452+08:00 level=INFO source=server.go:497 msg="loading model" "model layers"=63 requested=-1
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA0 size="10.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA1 size="11.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA2 size="11.8 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA3 size="11.3 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA4 size="12.3 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA5 size="12.0 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA6 size="12.0 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:240 msg="model weights" device=CUDA7 size="12.1 GiB"
time=2026-03-24T11:31:19.455+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA0 size="224.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA1 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA2 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA3 size="224.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA4 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA5 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA6 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:251 msg="kv cache" device=CUDA7 size="256.0 MiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA0 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA1 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA2 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA3 size="1.9 GiB"
time=2026-03-24T11:31:19.456+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA4 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA5 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA6 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:262 msg="compute graph" device=CUDA7 size="1.9 GiB"
time=2026-03-24T11:31:19.457+08:00 level=INFO source=device.go:272 msg="total memory" size="111.4 GiB"
time=2026-03-24T11:31:20.810+08:00 level=INFO source=runner.go:965 msg="starting go runner"
load_backend: loaded CPU backend from C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama\ggml-cpu-haswell.dll
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 8 CUDA devices:
Device 0: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36
Device 1: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-f7ad384d-30ee-b723-a586-06a5b29b8900
Device 2: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-8c020d1f-280d-e705-8f69-3a5342688f1a
Device 3: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538
Device 4: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-36588785-9363-4c15-053d-05548b16e1a1
Device 5: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0
Device 6: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e
Device 7: Quadro RTX 6000, compute capability 7.5, VMM: yes, ID: GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e
load_backend: loaded CUDA backend from C:\Users\its\AppData\Local\Programs\Ollama\lib\ollama\cuda_v13\ggml-cuda.dll
time=2026-03-24T11:31:21.082+08:00 level=INFO source=ggml.go:104 msg=system CPU.0.SSE3=1 CPU.0.SSSE3=1 CPU.0.AVX=1 CPU.0.AVX2=1 CPU.0.F16C=1 CPU.0.FMA=1 CPU.0.BMI2=1 CPU.0.LLAMAFILE=1 CPU.1.LLAMAFILE=1 CUDA.0.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.0.USE_GRAPHS=1 CUDA.0.PEER_MAX_BATCH_SIZE=128 CUDA.1.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.1.USE_GRAPHS=1 CUDA.1.PEER_MAX_BATCH_SIZE=128 CUDA.2.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.2.USE_GRAPHS=1 CUDA.2.PEER_MAX_BATCH_SIZE=128 CUDA.3.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.3.USE_GRAPHS=1 CUDA.3.PEER_MAX_BATCH_SIZE=128 CUDA.4.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.4.USE_GRAPHS=1 CUDA.4.PEER_MAX_BATCH_SIZE=128 CUDA.5.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.5.USE_GRAPHS=1 CUDA.5.PEER_MAX_BATCH_SIZE=128 CUDA.6.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.6.USE_GRAPHS=1 CUDA.6.PEER_MAX_BATCH_SIZE=128 CUDA.7.ARCHS=750,800,860,870,890,900,1000,1030,1100,1200,1210 CUDA.7.USE_GRAPHS=1 CUDA.7.PEER_MAX_BATCH_SIZE=128 compiler=cgo(clang)
time=2026-03-24T11:31:21.085+08:00 level=INFO source=runner.go:1001 msg="Server listening on 127.0.0.1:62697"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=runner.go:895 msg=load request="{Operation:commit LoraPath:[] Parallel:8 BatchSize:512 FlashAttention:Auto KvSize:8192 KvCacheType: NumThreads:28 GPULayers:63[ID:GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 Layers:7(0..6) ID:GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 Layers:8(7..14) ID:GPU-8c020d1f-280d-e705-8f69-3a5342688f1a Layers:8(15..22) ID:GPU-36588785-9363-4c15-053d-05548b16e1a1 Layers:8(23..30) ID:GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 Layers:8(31..38) ID:GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e Layers:8(39..46) ID:GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e Layers:8(47..54) ID:GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 Layers:8(55..62)] MultiUserCache:false ProjectorPath: MainGPU:0 UseMmap:false}"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=server.go:1350 msg="waiting for llama runner to start responding"
time=2026-03-24T11:31:21.089+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server loading model"
ggml_backend_cuda_device_get_memory device GPU-73d44d75-0d12-df63-c91d-ab76ac0c8b36 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA0 (Quadro RTX 6000) (0000:04:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-f7ad384d-30ee-b723-a586-06a5b29b8900 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA1 (Quadro RTX 6000) (0000:05:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-8c020d1f-280d-e705-8f69-3a5342688f1a utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA2 (Quadro RTX 6000) (0000:08:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-36588785-9363-4c15-053d-05548b16e1a1 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA4 (Quadro RTX 6000) (0000:84:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-57ab7e6f-39e8-2f43-7071-5eb9bfc8a9d0 utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA5 (Quadro RTX 6000) (0000:85:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-cc5bbee3-57ee-b847-ba4e-1f3847a4325e utilizing NVML memory reporting free: 24976601088 total: 25769803776
llama_model_load_from_file_impl: using device CUDA7 (Quadro RTX 6000) (0000:89:00.0) - 23819 MiB free
ggml_backend_cuda_device_get_memory device GPU-61a8ad69-4903-8ef4-d663-ad91c49fc24e utilizing NVML memory reporting free: 24959668224 total: 25769803776
llama_model_load_from_file_impl: using device CUDA6 (Quadro RTX 6000) (0000:88:00.0) - 23803 MiB free
ggml_backend_cuda_device_get_memory device GPU-1c1cd6d7-5b20-236b-70b9-78cb46647538 utilizing NVML memory reporting free: 24738156544 total: 25769803776
llama_model_load_from_file_impl: using device CUDA3 (Quadro RTX 6000) (0000:09:00.0) - 23592 MiB free
llama_model_loader: loaded meta data with 53 key-value pairs and 809 tensors from C:\Users\its\.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf (version GGUF V3 (latest))
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = minimax-m2
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.sampling.top_k i32 = 40
llama_model_loader: - kv 3: general.sampling.top_p f32 = 0.950000
llama_model_loader: - kv 4: general.sampling.temp f32 = 1.000000
llama_model_loader: - kv 5: general.name str = Minimax-M2.5
llama_model_loader: - kv 6: general.basename str = Minimax-M2.5
llama_model_loader: - kv 7: general.quantized_by str = Unsloth
llama_model_loader: - kv 8: general.size_label str = 256x4.9B
llama_model_loader: - kv 9: general.license str = other
llama_model_loader: - kv 10: general.license.name str = modified-mit
llama_model_loader: - kv 11: general.license.link str = https://github.com/MiniMax-AI/MiniMax...
llama_model_loader: - kv 12: general.repo_url str = https://huggingface.co/unsloth
llama_model_loader: - kv 13: general.base_model.count u32 = 1
llama_model_loader: - kv 14: general.base_model.0.name str = MiniMax M2.5
llama_model_loader: - kv 15: general.base_model.0.organization str = MiniMaxAI
llama_model_loader: - kv 16: general.base_model.0.repo_url str = https://huggingface.co/MiniMaxAI/Mini...
llama_model_loader: - kv 17: general.tags arr[str,2] = ["unsloth", "text-generation"]
llama_model_loader: - kv 18: minimax-m2.block_count u32 = 62
llama_model_loader: - kv 19: minimax-m2.context_length u32 = 196608
llama_model_loader: - kv 20: minimax-m2.embedding_length u32 = 3072
llama_model_loader: - kv 21: minimax-m2.feed_forward_length u32 = 1536
llama_model_loader: - kv 22: minimax-m2.attention.head_count u32 = 48
llama_model_loader: - kv 23: minimax-m2.attention.head_count_kv u32 = 8
llama_model_loader: - kv 24: minimax-m2.rope.freq_base f32 = 5000000.000000
llama_model_loader: - kv 25: minimax-m2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 26: minimax-m2.expert_count u32 = 256
llama_model_loader: - kv 27: minimax-m2.expert_used_count u32 = 8
llama_model_loader: - kv 28: minimax-m2.expert_gating_func u32 = 2
llama_model_loader: - kv 29: minimax-m2.attention.key_length u32 = 128
llama_model_loader: - kv 30: minimax-m2.attention.value_length u32 = 128
llama_model_loader: - kv 31: minimax-m2.expert_feed_forward_length u32 = 1536
llama_model_loader: - kv 32: minimax-m2.rope.dimension_count u32 = 64
llama_model_loader: - kv 33: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 34: tokenizer.ggml.pre str = minimax-m2
llama_model_loader: - kv 35: tokenizer.ggml.tokens arr[str,200064] = ["Ā", "ā", "Ă", "ă", "Ą", "ą", ...
llama_model_loader: - kv 36: tokenizer.ggml.token_type arr[i32,200064] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 37: tokenizer.ggml.merges arr[str,199744] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "e r...
llama_model_loader: - kv 38: tokenizer.ggml.bos_token_id u32 = 200034
llama_model_loader: - kv 39: tokenizer.ggml.eos_token_id u32 = 200020
llama_model_loader: - kv 40: tokenizer.ggml.unknown_token_id u32 = 200021
llama_model_loader: - kv 41: tokenizer.ggml.padding_token_id u32 = 200004
llama_model_loader: - kv 42: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 43: tokenizer.chat_template str = {# Unsloth template fixes #}\n{# -----...
llama_model_loader: - kv 44: general.quantization_version u32 = 2
llama_model_loader: - kv 45: general.file_type u32 = 12
llama_model_loader: - kv 46: quantize.imatrix.file str = MiniMax-M2.5-GGUF/imatrix_unsloth.gguf
llama_model_loader: - kv 47: quantize.imatrix.dataset str = unsloth_calibration_MiniMax-M2.5.txt
llama_model_loader: - kv 48: quantize.imatrix.entries_count u32 = 496
llama_model_loader: - kv 49: quantize.imatrix.chunks_count u32 = 81
llama_model_loader: - kv 50: split.no u16 = 0
llama_model_loader: - kv 51: split.tensors.count i32 = 809
llama_model_loader: - kv 52: split.count u16 = 0
llama_model_loader: - type f32: 373 tensors
llama_model_loader: - type q3_K: 173 tensors
llama_model_loader: - type q4_K: 232 tensors
llama_model_loader: - type q5_K: 20 tensors
llama_model_loader: - type q6_K: 11 tensors
print_info: file format = GGUF V3 (latest)
print_info: file type = Q3_K - Medium
print_info: file size = 94.33 GiB (3.54 BPW)
load: special_eos_id is not in special_eog_ids - the tokenizer config may be incorrect
load: printing all EOG tokens:
load: - 200004 ('<fim_pad>')
load: - 200005 ('<reponame>')
load: - 200020 ('[e~[')
load: special tokens cache size = 54
load: token to piece cache size = 1.3355 MB
print_info: arch = minimax-m2
print_info: vocab_only = 0
print_info: no_alloc = 0
print_info: n_ctx_train = 196608
print_info: n_embd = 3072
print_info: n_embd_inp = 3072
print_info: n_layer = 62
print_info: n_head = 48
print_info: n_head_kv = 8
print_info: n_rot = 64
print_info: n_swa = 0
print_info: is_swa_any = 0
print_info: n_embd_head_k = 128
print_info: n_embd_head_v = 128
print_info: n_gqa = 6
print_info: n_embd_k_gqa = 1024
print_info: n_embd_v_gqa = 1024
print_info: f_norm_eps = 0.0e+00
print_info: f_norm_rms_eps = 1.0e-06
print_info: f_clamp_kqv = 0.0e+00
print_info: f_max_alibi_bias = 0.0e+00
print_info: f_logit_scale = 0.0e+00
print_info: f_attn_scale = 0.0e+00
print_info: n_ff = 1536
print_info: n_expert = 256
print_info: n_expert_used = 8
print_info: n_expert_groups = 0
print_info: n_group_used = 0
print_info: causal attn = 1
print_info: pooling type = 0
print_info: rope type = 2
print_info: rope scaling = linear
print_info: freq_base_train = 5000000.0
print_info: freq_scale_train = 1
print_info: n_ctx_orig_yarn = 196608
print_info: rope_yarn_log_mul= 0.0000
print_info: rope_finetuned = unknown
print_info: model type = 230B.A10B
print_info: model params = 228.69 B
print_info: general.name = Minimax-M2.5
print_info: vocab type = BPE
print_info: n_vocab = 200064
print_info: n_merges = 199744
print_info: BOS token = 200034 ']~!b['
print_info: EOS token = 200020 '[e~['
print_info: UNK token = 200021 ']!d~['
print_info: PAD token = 200004 '<fim_pad>'
print_info: LF token = 10 'Ċ'
print_info: FIM PRE token = 200001 '<fim_prefix>'
print_info: FIM SUF token = 200003 '<fim_suffix>'
print_info: FIM MID token = 200002 '<fim_middle>'
print_info: FIM PAD token = 200004 '<fim_pad>'
print_info: FIM REP token = 200005 '<reponame>'
print_info: EOG token = 200004 '<fim_pad>'
print_info: EOG token = 200005 '<reponame>'
print_info: EOG token = 200020 '[e~['
print_info: max token length = 256
load_tensors: loading model tensors, this can take a while... (mmap = false)
load_tensors: offloading 62 repeating layers to GPU
load_tensors: offloading output layer to GPU
load_tensors: offloaded 63/63 layers to GPU
load_tensors: CPU model buffer size = 329.70 MiB
load_tensors: CUDA0 model buffer size = 11054.66 MiB
load_tensors: CUDA1 model buffer size = 12107.34 MiB
load_tensors: CUDA2 model buffer size = 12093.41 MiB
load_tensors: CUDA3 model buffer size = 11536.70 MiB
load_tensors: CUDA4 model buffer size = 12552.41 MiB
load_tensors: CUDA5 model buffer size = 12251.66 MiB
load_tensors: CUDA6 model buffer size = 12260.34 MiB
load_tensors: CUDA7 model buffer size = 12409.91 MiB
llama_context: constructing llama_context
llama_context: n_seq_max = 8
llama_context: n_ctx = 8192
llama_context: n_ctx_seq = 1024
llama_context: n_batch = 4096
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = auto
llama_context: kv_unified = false
llama_context: freq_base = 5000000.0
llama_context: freq_scale = 1
llama_context: n_ctx_seq (1024) < n_ctx_train (196608) -- the full capacity of the model will not be utilized
llama_context: CUDA_Host output buffer size = 6.20 MiB
llama_kv_cache: CUDA0 KV buffer size = 224.00 MiB
llama_kv_cache: CUDA1 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA2 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA3 KV buffer size = 224.00 MiB
llama_kv_cache: CUDA4 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA5 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA6 KV buffer size = 256.00 MiB
llama_kv_cache: CUDA7 KV buffer size = 256.00 MiB
llama_kv_cache: size = 1984.00 MiB ( 1024 cells, 62 layers, 8/8 seqs), K (f16): 992.00 MiB, V (f16): 992.00 MiB
llama_context: pipeline parallelism enabled (n_copies=4)
llama_context: Flash Attention was auto, set to enabled
CUDA error: out of memory
current device: 6, in function alloc at C:\a\ollama\ollama\ml\backend\ggml\ggml\src\ggml-cuda\ggml-cuda.cu:576
cuMemAddressReserve(&pool_addr, CUDA_POOL_VMM_MAX_SIZE, 0, 0, 0)
C:\a\ollama\ollama\ml\backend\ggml\ggml\src\ggml-cuda\ggml-cuda.cu:94: CUDA error
time=2026-03-24T11:32:15.465+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server not responding"
time=2026-03-24T11:32:17.522+08:00 level=INFO source=server.go:1384 msg="waiting for server to become available" status="llm server error"
time=2026-03-24T11:32:17.615+08:00 level=ERROR source=server.go:303 msg="llama runner terminated" error="exit status 1"
time=2026-03-24T11:32:17.772+08:00 level=INFO source=sched.go:511 msg="Load failed" model=C:\Users\its\.ollama\models\blobs\sha256-d98bf8c3c536de17d554ba4a78919ea45717074fbb7184cdd1f0d3bbdca055bf error="llama runner process has terminated: CUDA error"
[GIN] 2026/03/24 - 11:32:17 | 500 | 1m2s | 127.0.0.1 | POST "/api/generate"OS
Windows
GPU
Nvidia
CPU
Intel
Ollama version
0.18.2
extent analysis
Fix Plan
To resolve the CUDA out-of-memory error, we'll focus on optimizing memory usage. Here are the steps:
- Reduce batch size: Lower the batch size to reduce memory requirements. You can do this by setting the
BatchSizeparameter in theloadrequest. - Disable pipeline parallelism: Pipeline parallelism can increase memory usage. You can disable it by setting
n_copies=1in thellama_context. - Reduce KV cache size: Decrease the KV cache size to free up memory. You can do this by setting the
KvSizeparameter in theloadrequest. - Use model pruning: If possible, use a pruned version of the model to reduce memory requirements.
- Update Ollama: Ensure you're running the latest version of Ollama, as updates may include memory optimization fixes.
Example code changes:
# Reduce batch size
load_request = {
"Operation": "commit",
"LoraPath": [],
"Parallel": 8,
"BatchSize": 256, # Reduced batch size
"FlashAttention": "Auto",
"KvSize": 4096,
"KvCacheType": "",
"NumThreads": 28,
"GPULayers": 63,
# ...
}
# Disable pipeline parallelism
llama_context = {
"n_seq_max": 8,
"n_ctx": 8192,
"n_ctx_seq": 1024,
"n_batch": 4096,
"n_ubatch": 512,
"causal_attn": 1,
"flash_attn": "auto",
"kv_unified": False,
"freq_base": 5000000.0,
"freq_scale": 1,
"n_copies": 1, # Disabled pipeline parallelism
# ...
}Verification
After applying these changes, restart the Ollama server and attempt to load the model again. Monitor the logs for any errors or memory-related issues. If the problem persists, you may need to further optimize memory usage or consider using a different model.
Extra Tips
- Regularly update your GPU drivers to ensure you have the latest optimizations and fixes.
- Consider using a tool like
nvidia-smito monitor GPU memory usage and identify potential bottlenecks. - If you're using a large model, consider using a model pruning technique to reduce memory requirements.
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- openai-codex subscription backend returns HTTP 200 with response.output=None, causing Slack/cron failures
- RFC: Centralized Model/Provider Registry
- bug: openai-codex provider — TypeError: 'NoneType' object is not iterable on every request (gpt-5.5)
- [Feature]: Source-aware instruction gate — architectural mitigation for indirect prompt injection
- Named custom provider stale_timeout_seconds ignored because runtime provider is normalized to `custom`
- guard test (ignore)
- [Feature]: per-platform LLM request_overrides (extra_body / reasoning_effort / service_tier)
- One-shot smoke: add Flue-backed orchestration fixture
- Gateway should not treat stale Codex app-server progress as final response after post-tool silence
- `docker_run_as_host_user: true` breaks bundled skills: Hermes home is mounted into `/root/.hermes` but the container runs as a non-root user (`HOME=/home/pn`)
- [Bug]: gateway api_server streaming bypasses server-side tool-call loop when chat_template_kwargs.enable_thinking=false (model emits tool name as plain text)
- [Feature]: Pre-install python-telegram-bot in Umbrel Hermes Docker image
- YouTube Shorts filter not working in youtube-content skill
- v0.15.0 PyPI release breaks ALL platforms — plugin.yaml manifests missing from package
- RFC: On-demand tool/skill/MCP discovery — decouple schema registration from process lifecycle
- Pixshelf: local-first stock photo workflow command center
- [Bug]: baoyu infographic skill should not silently bypass image_generate
- Pixshelf v1.5: manual submission tracking for stock agencies
- `hermes config set` silently accepts unknown keys, writing them where the runtime never reads
- Honcho memory prefetch hang on fresh CLI subprocess in v0.15.0 (regression from #27190)
- [Bug] v0.15.0 Docker image: stage2-hook.sh, main-wrapper.sh missing; container_boot module removed
- Feature: Reduce cache-read token overhead for DeepSeek providers — configurable cache_ttl, skills snapshot trimming, memory compaction
- Windows: three bugs from daily use (plugin discovery, gateway exit code, Unicode decode
- holographic memory: HRR silently degrades to FTS5 when numpy is missing
- Make max_tokens configurable for aux vision calls
- Conversation compression desynchronizes session ID between agent context and gateway routing, causing silent message loss
- [Bug]: v0.15.0 Docker image:The TUI cannot be used in the dashboard.
- cron: skip_memory=True blocks fact_store/memory tools from all cron jobs
- TUI: Node.js OOM crash when agent uses browser tools repeatedly
- feat: model_profiles — per-model toolset and memory config
- Automatic background skill patching disrupts active sessions (severe impact on local models)
- ensure_hermes_home() creates root-owned dirs in profile subdirectories when kanban workers are dispatched
- Feature: opt-in webhook bypass for DISCORD_ALLOW_BOTS — allow operator-initiated probes without weakening bot-loop guard
- v0.15.0: Codex requests fail HTTP 400 when participant display_name contains non-ASCII (emoji breaks input[].name pattern)
- Architecture: State Persistence Precedence (Memory vs Skills vs Hooks)
- [Bug]: cronjob tool: create action always fails with "schedule is required for create" even when parameters are provided
- codex-oauth: 'NoneType' object is not iterable in _run_codex_stream (gpt-5.5) — every turn fails non-retryably
- Docs/Config: Plugin local scope enablement ambiguity
- [Bug]: CLI freezes after using /new command (WSL)
- Profile Codex auth can ignore global credential pool when local state is stale
- [workflow-engine] CRITICAL: variable substitution crashes on regex metachars in user input
- [workflow-engine] HIGH: loop and bash nodes leak subprocesses on timeout
- [workflow-engine] HIGH: README documents config env vars the engine never reads
- [workflow-engine] MEDIUM: workflow_run rate limit bypassable via concurrent calls (TOCTOU)
- [workflow-engine] chore: manifest gaps, side-effectful register(), dead code, unauth kanban dispatch
- [mcp_lazy] HIGH: synthetic mcp_server_<name> stub collides with a real MCP server named 'server'
- [mcp_lazy] HIGH: promote_server eager flag documented but never persisted
- [mcp_lazy] MEDIUM: _prev_mode dict leaks and goes stale; not cleared on session evict
- [mcp_lazy] MEDIUM: get_pool has unlocked check-then-set race on pool creation
- [mcp_lazy] MEDIUM: pre_tool_call gives no guidance for unpromoted server-stub calls
- [mcp_lazy] chore: undeclared pre_tool_call hook, nonexistent 'mcp_load_tools' name in docs, missing tests
- [a2a_fleet] CRITICAL: server never auto-starts — register() runs outside an event loop
- [a2a_fleet] CRITICAL: auth_required defaults to false on a cross-machine surface
- [a2a_fleet] HIGH: remove invented disable() hook — loader never calls it, port leaks on reload
- [a2a_fleet] HIGH: plugin.yaml missing kind / provides_tools / requires_env (token env undeclared)
- [a2a_fleet] MEDIUM: tighten wide-open CORS, anonymous /health peer leak, and peer-URL SSRF
- [a2a_fleet] MEDIUM: relocate tests to tests/plugins/ and cover sync-register + auth-default paths
- xai-oauth auxiliary client incorrectly uses Responses API (CodexAuxiliaryClient), causing 403 on compression/vision/web_extract
- [Bug]: Direct Copilot gpt-5.5 large resumes are killed by 12s Codex TTFB watchdog
- [Bug]: `hermes uninstall` does not work on Windows
- TUI: Thinking block leaks raw JSON and Σ character
- Hostinger VPS: migration Hermes Agent → Hermes WebUI impossible (tini + UID mismatch + sessions)
- /goal judge over-continues exploratory goals unless the assistant explicitly says the goal is complete
- /goal auto-continuation can be amplified by preflight compression/session split and resurrect stale task state
- Dashboard infinite reload loop in loopback mode — GET /api/auth/me returns 401 on every page load
- [Bug]: Provider/LLM switch leaves stale encrypted_content causing 400 errors on Telegram sessions
- [Bug]: Infinite reload loop / React state loop on Sessions tab (Firefox + Chrome) — repeated 401 on /api/auth/me (v0.15.0)
- show_reasoning should work independently of streaming in CLI mode
- Feature Request: Strip reasoning/<think> blocks from TTS preprocessing
- mcp add / mcp test raise NameError when mcp package not installed
- v0.14.0 dashboard breaks behind reverse proxies — two regressions
- Skills hub creates empty category directories when no skills installed
- [Bug]: Custom endpoint: ChatCompletions returns content, but Hermes treats response as empty (v0.14.0)
- fix: atomic_replace() fails with EXDEV when HERMES_HOME is a cross-filesystem symlink
- fix(gateway): Feishu session cancellation orphans session guard, permanently blocking messages
- Custom endpoint pricing can overestimate Crof qwen3.5-9b cost by 1,000,000x
- MCP OAuth callback: module-level port global causes port collisions and structural weaknesses vs upstream
- Bug: send_message tool bypasses validate_media_delivery_path security check
- Proposal: Add Mnemosyne to official memory provider documentation
- feat(swarm): support custom verifier/synthesizer body + skills
- Template conversion failed
- Error occurred in the operation of the agent node in the workflow.
- PubSub client overrides Sentinel client when REDIS_USE_SENTINEL is enabled
- Frontend description of the Retrieval node output does not match the actual output
- JSON type input var raise Intenal server error
- cannot extract elements from a scalar
- 负载均衡 为模型配置多组凭据，并自动调用，此功能无法选择
- add models is error
- panic: could not create filter
- Persist partially generated messages when /chat-messages/:task_id/stop is called
- MCP server connection fails with 403 — request never leaves Dify (SSRF proxy suspected)
- Support durable async execution backends for long-running workflow steps
- [Xiaomi MiMo] Credentials validation fails with 400 "Not supported model mimo-v2-flash" when using Token Plan endpoint (v0.0.7)
- After clicking preview on a parent-child segmented knowledge base, it shows 0 chunks
- Retrieval score differs between UI upload (.docx) and API upload (.txt) despite identical chunk content and embedding model
- gemini cli crash again
- Xbox gift card code damage
- Damage caused by the gemini cli crash
- ioctl(2) failed, EBADF (Bad File Descriptor)
- Feat: Support Bun as an alternative runtime/package manager for updates and extensions
- fatal error again!!!!
- ioctl error
- Critical Crash: ioctl(2) failed, EBADF in ShellExecutionService.resizePty
- ioctl(2) failed, EBADF
- v0.44.0 Regression: Critical crash with ioctl(2) failed, EBADF during PTY resize
- Crash on startup: ioctl(2) failed, EBADF in UnixTerminal.resize
- Crash: `ioctl(2) failed, EBADF` in `node-pty` during PTY resize on macOS
- Gemini CLI crashes with `ioctl(2) failed, EBADF` in `node-pty` during `resizePty`
- Remote Role
- ERROR ioctl(2) failed, EBADF /home/mich
- RangeError: Maximum call stack size exceeded
- EBADF Error during folder creationg broke session and terminal glitches
- MAIP / Gargoub Project - Mediterania - North Coast
- Gemini cli crash again in this morning
- ERROR ioctl(2) failed, EBADF
- Verified node install fails — Checksum verification failed (Cloud)
- The extended debugging key did not arrive during registration.
- CollaborationPane unmounts collaboration store on single-user instances, causing permanent "No network connection" state
- Workflow cannot be saved when the name contains "->" (Potentially malicious string)
- automation does not work and does not show an error
- Raj Ai Automation
- Default Data Loader: DOMMatrix is not defined error
- Feature: Per-node execution timestamp overlay on canvas during workflow run
- AI Agent + Vertex `gemini-3.5-flash`: 400 "missing thought_signature" on sequential multi-turn tool calls (post-#24982)
- PDF Loader in Pinecone Vector Store fails due to pdf-parse version conflict (v2 not supported)
- emailReadImap: add UID deduplication, batch size cap, and numeric uid enforcement
- Manual node execution fails with "Could not find a node" when autosave is disabled (N8N_WORKFLOWS_AUTOSAVE_DISABLED)
- Schedule Trigger stopped firing — workflow Published & active, manual executions succeed, no automated fires for 2+ hours
- [MCP SDK] create_workflow_from_code intermittently returns HTTP 500, often as a false negative (workflow persists anyway, causing duplicates on retry)
- Credential-load wedge: workflows using googleApi/jwtAuth credentials silently fail to execute after key rotation
- Google Sheets Trigger every minute is not working manual Execute is working sent email
- [BUG] Plugin marketplace MCP connector remains stuck "still connecting" when mcp-remote requires OAuth
- [redacted at user request]
- Opus 4.7 behavioral regression: loaded instruction-following discipline degraded in recent Claude Code/Cowork updates
- [BUG] Tailscale via Homebrew CLI + Mac App Store GUI, both Macs on macOS, Cowork blocked by VPN detector despite Tailscale being a mesh VPN with no traffic interception
- stopShellPty on tab switch kills active sessions (exit 143) — regression in May 27 build
- [BUG] Long URLs are broken into multiple lines and become unclickable in terminal output
- [BUG] claude rm/stop/reap SIGKILLs background session tree without SIGTERM grace, orphaning git index.lock and similar
- [BUG] Default git workflow in the system prompt was pushed without context or consent
- [MODEL] Inconsistent output quality / Ignoring instructions (overfitting and inappropriate repetition of Korean vocabulary)
- You've hit your weekly limit · resets May 31 at 5pm (Asia/Shanghai)
- Paid yearly subscription silently downgraded to Free with no user action
- [Regression v2.1.153] Plugin bash hooks fail with "echo: write error: Permission denied" on Windows (claude-mem, shell: "bash")
- [BUG] Connector toggles in conversation are not clickable — must click text label instead
- [remote-control] Input from mobile app/browser not reaching host session — output works fine
- Model fails to read/reference CLAUDE.md contents despite being loaded in context
- [BUG] Claude Desktop reinstall destroys Code chat history (transcripts + Recents) while regular Chat history, project files, and memory all survive
- Bypass mode clamps to Accept Edits even with the toggle ON (Claude Code Desktop 1.9255.2 / CC 2.1.149)
- [BUG] TUI input freezes randomly mid-typing — entire prompt becomes unresponsive for minutes
- [BUG] Cowork downloads Linux ELF binary instead of macOS binary on macOS Sonoma 14.8.7 — exit code 132 (SIGILL) on every session
- [Feature Request] Persistent project memory — sessions forget everything on close, forcing users to keep many sessions open
- [Bug] Thread context stale after sleep/resume, returns outdated date and calendar data
- [FEATURE] Add context window usage indicator and warning before auto-compaction
- [BUG] Dictation error: Invalid character in header content ["x-config-keyterms"] on Windows
- [Bug] Anthropic API Error: Server rate limiting despite normal usage
- Does delegating work to `claude -p` subprocesses reduce context accumulation in the parent session?
- [BUG] Claude Code hangs on M1 Mac when terminal says "opening browser to sign in" and browser opens
- [BUG] Claude_Preview MCP preview_start spawns dev server with main-repo cwd instead of session's worktree cwd
- [Bug] Anthropic API Error: Server rate limiting during request execution
- [Bug] Anthropic API Error: Server rate limiting on concurrent requests
- [Bug] Ultraplan ready notification fires before cloud agent completes execution
- [BUG] API 500 ERROR ALL THROUGHOUT THE DAY
- [BUG] Cowork: Live Artifacts folder path changed in 1.9255.2, no automatic migration from Documents\Claude\Artifacts
- [Bug] Auto-compact never triggers despite statusline reporting "100% context used" (v2.1.153, Max sub, 200K mode)
- [BUG] [Desktop / macOS] 'Open in → New Window' detached session: font renders smaller than main, no per-window controls, Cmd+/Cmd- keystrokes routed to main window instead
- Feature request: option to switch between classic and new minimal UI
- [Feature Request] Show timestamps for each message
- [BUG] Terminal corruption when permission prompt appears while navigating Agent Teams agent selection menu
- [FEATURE] Allow users to customize the background color of the Claude desktop app beyond the current light/dark theme presets.
- [BUG] Statusline not displaying on Windows [fixed]
- Background agent UI Stop button is a no-op for stuck agents — process keeps consuming tokens
- Background agents silently die on session pause/resume — no completion notification, no work recovery
- Add option to hide email address from welcome banner
- [BUG] SSH Remote: `projects` field in remote ~/.claude.json becomes null after desktop restart — jsonl files intact, UI shows 'No messages yet' for every session
- [Bug] Claude Code not applying fixes despite claiming to complete tasks
- billing is unfair and poorly documented
- [BUG] Claude Code on the web: declared plugins inactive on first session, require restart to fully load
- [BUG] Restore from archive deleted sessions instead of restoring them
- [BUG] M365 connector fails with AADSTS50011 in Cowork — localhost vs 127.0.0.1 redirect URI mismatch
- claude agents: workflow slash-commands missing from dispatch-input completion (regression-adjacent to #61424)
- Claude Desktop's Info.plist missing TCC usage strings, blocks all EventKit-based MCP servers
- False-positive safety blocks on self-administered governance amendments — request for owner-authority mode for verified professional users
- [BUG] Stop pushing "AUTO"-mode
- [DOCS] Plugin marketplace guide omits `skipLfs` option for git-based sources
- [DOCS] MCP docs omit combined startup notification for MCP server and connector authentication
- [DOCS] Agent view docs omit macOS Privacy & Security identity for background agents
- [DOCS] Npm update docs do not explain release-channel behavior for `claude update`
- [DOCS] Agent SDK docs omit `subagent_type: "claude"` worktree and output persistence behavior
- [DOCS] Background session docs omit `$CLAUDE_JOB_DIR` temp-file behavior
- [FR] mask env-var values in 'claude mcp get <server>' output
- [FR] subagent worktrees should not inherit stale local 'user.email' from prior dispatches
- [BUG] Windows: Grep tool leaks rg.exe + conhost.exe processes (~2000 zombies / 14 GB RAM in long sessions)
- [BUG] Stats dashboard "Peak hour" appears off by one hour
- [BUG] Diff highlight (teal SGR background) bleeds past changed text in 2.1.150–2.1.153
- [FEATURE] confirm before deleting session
- Plugin PostToolUse hooks still silently skip in Claude Desktop / Cowork (re-filing closed #51904)
- /code-review skill: silent fallback to main...HEAD reviews other people's commits, and JSON-only output is hard to read
- Monitor tool doesn't source the shell snapshot like Bash does; PATH-dependent tools (jq, sleep, etc.) fail in Monitor commands on macOS/Nix
- [Bug] Long input lines truncated with ellipsis while typing instead of wrapping in terminal UI
- [FEATURE] VS Code extension: Render submitted user messages as Markdown in chat
- OSC 52 copy from Claude TUI doesn't reach clipboard inside tmux (regression in 2.1.146–2.1.153)
- [BUG] RemoteTrigger create/update returns HTTP 400 with circular error: "event_type is required" / "unknown field event_type"
- [BUG] Option to hide or minimize the built-in "status footer" (multi-line debug/cost panel) [re-raise of #31475]
- [Bug] Feedback submissions being closed without review or action
- [FEATURE] Word-jump cursor navigation in Chat input (option+arrow / bindable actions)
- [FEATURE] ! shell mode: filesystem tab completion
- [BUG] API Error: Usage credits required for 1M context
- claude agents: OSC 52 clipboard emission broken in tmux (regression in 2.1.146–2.1.153)
- CLI crashes on macOS 15 M3 - exit code 1
- [FEATURE] Support Cmd+V image paste from clipboard
- [FEATURE] Enhance claude.ai M365 connector to support MS Planner
- [BUG] Slash command autocomplete hijacks pasted absolute file paths starting with /
- PreToolUse hook `if` filter false-positives on complex Bash commands
- [BUG] Diff panel hangs/whites out
- Feature Request: Support drag-and-drop for binary documents (.wps, .doc, .docx, .xlsx, .pdf) in VS Code extension
- [BUG] activation of 1M context in VSCode
- [FEATURE] Support i18n / language localization for built-in slash command outputs
- Ctrl+V para colar imagens deixou de funcionar no CLI (Windows, PowerShell)
- [FEATURE] Please add Norwegian (Bokmål/Nynorsk) language support to the Claude Code interface
- [BUG] OTel log events (claude_code.user_prompt, api_request_body, tool_decision, hook_execution_complete) emitted with empty trace_id/span_id while sibling spans correlate correctly
- [BUG] Cowork crashes on every message, no VM logs generated, missing AppData\Roaming\Claude
- [FEATURE] first-class session handoff + per-session token budgets for unattended runs
- [FEATURE] Smart paste: convert clipboard code to file reference chips (like Cursor)
- [Feature Request] Restore chat pin functionality to title chat submenu
- [BUG] SIGILL issues with version 2.1.153
- [BUG] Cowork plugin upload fails with generic "Plugin validation failed" when a `description` field in any SKILL.md frontmatter contains angle brackets (`<…>`)
- [BUG] Desktop App 2.1.144+: startup scanner deletes cliSessionId from claude-code-sessions local files on every launch — session not found on disk
- [Feature Request] Add keyboard shortcut to copy last message with proper formatting
- [MODEL] Opus 4.7 not 1M
- Allow naming/renaming background agents in `claude agents` view
- Stale worktrees in .claude/worktrees/ are never cleaned up, consuming massive disk space
- Agent worktrees are never cleaned up, silently consuming disk space
- Subagent worktrees not auto-cleaned when reviewer writes scratch files
- [Bug] Skill initialization hangs for extended duration in Plan Mode
- Claude Desktop writes malformed registry Run entry (nested escaped quotes) - crashes Windows Task Manager and other Run-key parsers
- IME candidate window shows at bottom-right corner instead of caret position (Windows CMD)
- [BUG] Pressing 'Escape' doesn't close the /BTW conversation when the main conversation is asking for approval
- [BUG] Opus 4.7 (1M) intermittently emits empty-string values for tool_use.input fields, killing the session
- FleetView agent UI shows "running" with incrementing elapsed time after agent has returned
- /doctor flags context-scoped cmd+c binding as macOS conflict (false positive)
- [BUG] Text Rendering in Elvish
- Desktop app: Bypass Permissions mode flips to Accept Edits on first prompt (M5 / macOS 26.5)
- [Workaround] Date-Weekday Verification Hook — Prevents Claude from writing wrong weekdays
- [BUG] Claude Code create c:/memfs directory without asking me.
- [BUG] Claude Code's Bash execution waits forever with no processes running
- [BUG] usage stays stuck waiting for 5 hr limit after upgrading to premium seat in team plan
- [Workflow tool] resume cache is unreachable for nontrivial workflows because LLM dispatchers can't transcribe args byte-exactly
- Code review (Preview): "Add a repository" shows no results for private GitHub org repos
- [BUG] /context commands blows up context
- [Feature Request] Add precache expiry hook to enable proactive compaction before token eviction
- [BUG] Context indicator shows 0% at session start despite ~20K+ tokens already loaded
- [Feature Request] Add semantic search for --resume session history
- [Feature Request] Add session search, tagging, and filtering capabilities
- [BUG] Cowork Dispatch reports "desktop not available" on Windows 11 while standard Cowork works normally
- [Bug] Claude Code provides incorrect suggestions with high confidence despite errors
- defaultMode: acceptEdits silently overrides per-path permissions.ask rules for Write/Edit
- [FEATUR configurable tip interval (e.g. tipIntervalSeconds: 30 in settings)E]
- Plugin marketplace fails to load: schema rejects 'displayName' key (v2.1.153)
- claude agents: in-session copy uses broken OSC 52 path while overview correctly uses tmux buffer
- [BUG] Plugin agent descriptions (and custom agents) load unconditionally into context — no parity with disable-model-invocation for skills
- Crashed ultrareview consumed a free credit despite producing zero findings
- [Bug] Character rendering issue - invisible or missing text display
- [BUG] Cowork: processo Claude Code encerra com código 3 — .claude.json não contém token de autenticação (Windows 11 25H2)
- [BUG] 2.1.153 silently discards tools/list response from rmcp 0.12.0 HTTP MCP server (works in 2.1.152, wire-identical handshake)
- VS Code extension: option to auto-resume last session when reopening a workspace folder
- [Bug] Conversation continuation failure
- [BUG] Cowork crashes every time I start a new chat or attempt to continue an existing one in any project. The error displayed is: "Claude Code è andato in crash
- [Bug] Unannounced quota changes
- Native update/install fails with 'socket connection was closed unexpectedly' behind proxy — undici TLS incompatibility
- [BUG] Session name reverting after manual change
- [BUG] 非正常思考，上下文过长时，一直显示思考，点击interrupt按钮失效
- Honor `tools:` frontmatter when an agent is invoked via `@mention` — strip `Task` only when the agent did not declare it
- macOS TCC popup still recurring on v2.1.153 — "2.1.153" would like to access data from other apps
- Claude Code leaks pty handles — exhausts pseudo-terminals on macOS after long session
- [Bug] Agent fails to execute or respond to user input
- [BUG] Persistent "Expecting value: line 1 column 1 (char 0)" JSON parse error after tool execution
- [Feature Request] Implement proactive unit test coverage recommendations for recurring bugs
- VS Code panel lacks status line + terminal lacks image paste in Codespaces, forcing a tradeoff
- `/powerup` only shows ~10 lessons — allow viewing the full catalog
- [Bug] Context contamination after auto-compact with unrelated email draft of Tejo/Sado Basin
- [Bug] VSCode terminal output displays corrupted text with garbled symbols
- [Feature Request] Add LaTeX/KaTeX math rendering to TUI
- [Bug] Sub-agent PR review results not validated by orchestrating agent
- Subagents on Pro 1M tier: trivial probes pass, real workloads fail at first tool call (probe-vs-workload divergence)
- Path-scoped rules and subdirectory CLAUDE.md not loaded when creating new files matching the pattern
- AskUserQuestion: cancelling during extended thinking poisons the whole session with 400 'thinking blocks cannot be modified' (2.1.153); concurrent prompts overwrite each other
- Ideas Missing from Claude Cowork Menu (Windows)
- [BUG_BOUNTY_SAFE_POC_2026] Prompt Injection RCE Test - Command Execution Proof
- [BUG] Cowork scheduled task: execution history row not showing after successful run
- Resuming an extended-thinking session fails permanently with 400 "thinking blocks cannot be modified" (transcript stores thinking text as empty but keeps signature)
- [Bug] Plugin-registered CwdChanged and FileChanged hooks don't fire (settings.json works) — v2.1.153
- Auto-archive on PR merge / branch delete — clarify autoArchiveSessions semantics or add dedicated opt-out
- `claude mcp add` echoes Authorization header value verbatim to stdout, leaks bearer tokens to terminal and session transcripts
- [BUG] Bug report — /insights skill, Claude Code The /insights skill outputs a malformed file path.
- Plugin slash commands render with '*'-inline format instead of two-column, despite matching official plugin shape
- [Bug] Unexpected long text generation without user input or goal
- [Bug] Thinking blocks causing task progression blocked without user modification
- [BUG] (Critical!) contamination by an unknown session simirlar to the report => [Bug] Context contamination after auto-compact with unrelated email draft of Tejo/Sado Basin #63137
- [Critical] Opus 4.7 Korean output degeneration — Korean grammar itself collapses in long contexts
- [BUG] Title: Autocompact buffer persists across /clear — wastes tokens for irrelevant old context
- [Bug] Auto-Compact loses user input before processing in conversation history
- Feature: per-invocation effort parameter + runtime session-config introspection for skills
- Auto-mode classifier mislabels Azure DevOps vote -5 as "Reject" when denying PR vote actions
- [BUG] Claude Desktop and Claude Code CLI never re-register MCP tools after OAuth 2.1 handshake on a remote HTTP server
- [BUG] Workspace file tags leak across sessions
- [BUG] Ink renderer crashes on Windows 11 build 26200 (Canary) duplicate banners, terminal mode leaks, mid-operation aborts
- [BUG] Claude Code Desktop issue
- PTY master fd leak in Claude desktop app exhausts macOS kern.tty.ptmx_max after ~2-3 days
- [BUG] Claude Code — Session Management after Unexpected Interruption
- [Windows] Cowork OpenTelemetry exporter does not initialize - zero events emitted to any destination, including loopback
- [Bug] Opus 4.7: 400 `thinking blocks ... cannot be modified` on long extended-thinking sessions, triggered by history-altering events (scheduled prompts / parallel tool-call cancellation)
- [BUG] API Error: Server is temporarily limiting requests (not your usage limit) · Rate limited
- Multi-plugin custom marketplace: only first plugin registered in installed_plugins.json, skills don't load
- [BUG] Git push through the SDK's git proxy fan-outs into ~500 GitHub REST API calls, exhausting the 5,000/hour budget after a handful of pushes
- [BUG] Claude took liberties it really shouldn't with my global config
- [BUG] Agent window focus lost after navigating with arrow keys, causing scroll deadlock
- [BUG] `--model` flag silently ignored in interactive sessions (works in `--print` only)
- [BUG] Dispatch permanently shows "desktop appears offline" on Windows 11 - never worked on first use
- feat: support per-command enableWeakerNetworkIsolation as safer alternative to dangerouslyDisableSandbox
- /code-review outputs a raw JSON array instead of readable findings
- [BUG] Cowork — Additional allowed domains ignored on Team plan; same domain works on Pro plan
- Haiku
- [Bug] False positive blocking beneficial outcomes in tool execution
- 3P Bedrock SSO: credentials silently expire without triggering re-auth on day 2+
- CLAUDE_AUTOCOMPACT_PCT_OVERRIDE in settings.json env block silently ignored by autocompact logic
- Auto-compaction deletes main session JSONL before verifying summary completion, causing data loss
- [Bug] Claude Code not executing stated actions or producing expected results
- [FEATURE] Deferred Messages — Queue Input for End of Turn
- [BUG] Up/Down arrows in input box navigate history instead of moving cursor — regression in 2.1.149+
- Cancelling a parallel tool-call batch corrupts thinking blocks -> 400 "thinking blocks cannot be modified" permanently wedges the session
- Claude Code caused data loss, then contradicted itself about recovery (two incidents, one session)
- [Bug] Unclear error messages from Claude Code CLI
- [Bug] Agent tool rejecting due to context size limit exceeded
- claude agents: daemon and bg-spare processes spin at ~100% CPU when idle
- [BUG] Compaction fails with "context window limit" error even when context usage is low (e.g., 20%) — regression in v2.1.153
- Remote Control entitlement lost after May 27-28 incident — `Error: Remote Control is not yet enabled for your account` on active Max subscription
- PreToolUse hook exit code 2 does not block Write tool
- [Bug] Thinking blocks in latest assistant message are immutable
- GUI: dispatch file:// and custom-scheme clicks to OS shell handler
- Show current model in statusLine by default
- [Bug] Agent console becomes unresponsive to keyboard input after multiple agents initialized
- [FEATURE] PreToolUse hooks should have a way of updating the environment
- [Bug] Unable to start or use Claude Code CLI
- [BUG] Repository not visible in Claude Code web repo picker
- Session permanently wedged on 400 "thinking blocks cannot be modified" after parallel tool_results
- [Bug] @ autocomplete loses sibling repos after a file edit in multi-repo workspace
- Unclear error message when creating sub-agent without authentication
- [Bug] Anthropic API errors causing frequent failures and high token usage
- [BUG] @ mention file picker only shows packages, not individual files (desktop app - Code tab)
- [Bug] TUI panel footer remains sticky and consumes excessive terminal space
- PR-status polling exhausts GitHub GraphQL rate limit on repos with many open PRs
- [BUG] Windows: welcome panel not shown in some project folders (2.1.153)
- [Bug] Anthropic API Error: thinking blocks corrupted during context compaction with extended thinking enabled
- API 400 "thinking blocks cannot be modified" permanently bricks session during agent activation (interleaved thinking + tool use)
- Right-click Copy copies the whole message instead of the selection; pasted text retains dark background
- Mid-session model switch corrupts conversation when extended thinking is enabled (API 400: 'thinking blocks cannot be modified')
- [BUG] Markdown file links in chat output do not open files when clicked (VS Code extension)
- Stuck retry loop: `400 thinking blocks cannot be modified` on large interleaved-thinking turns using AskUserQuestion
- [FEATURE] Prompt user for approval before auto-compaction proceeds
- Custom MCP connectors not attachable to scheduled routines — no UUID discovery path
- [BUG] Claude in Chrome — Navigation blocked for teams.cloud.microsoft and outlook.cloud.microsoft after Microsoft domain migration**
- [BUG] Claude Desktop — Personal plugins panel renders list but is entirely non-interactive (macOS, v1.9255.2)
- [Bug] error when using Workflows
- [BUG] Persistent "update available" notification despite being on latest version
- [BUG] Sweep Agent from /code-review never completes
- [Bug] Tool calls not executing or returning results
- [FEATURE] Cloud-synced memory and settings across machines
- [Bug] Terminal UI freezes when Ctrl+O view exits during interactive prompt in plan mode
- Continuous api errors when using claude code with Opus 4.7 with thinking on low
- [Feature Request] Add support for installing and using previous Claude Code versions
- [Bug] Extended Thinking: Summarized thinking blocks fail signature validation when resent to API
- [Bug] Anthropic API Error: 'thinking' blocks cannot be modified
- [Bug] Anthropic API Error: Thinking blocks cannot be modified with extended thinking mode
- Feature request: Lazy/on-demand MCP server connections
- [Bug] Tool Arguments Parsed as String Instead of Object
- [Bug] Anthropic API Error: Insufficient context provided
- [Bug] Claude Opus occasionally uses moskovian(russian) orthography instead of Ukrainian in system-prompted responses
- Opus 4.8: backgrounded task completions (subagents AND Bash) crash with 400 "thinking blocks cannot be modified"
- [Bug] Opus 4.7 fabricates stable preferences ("my default") to rationalize arbitrary choices when challenged
- [Bug] Unable to update Claude Code CLI
- [BUG] Desktop app: /remote-control mints link + connects bridge (main.log) but in-chat link/QR panel never renders
- Feature: sessionColor and sessionName in .claude/settings.json
- [BUG] Anthropic API error: thinking blocks
- [FEATURE] Support Remote MCPs in Cowork as in Claude Code
- [Bug] Anthropic API Error: 400 Bad Request with Redacted Thinking - 0 4.7 & 4.8
- [Bug] Anthropic API Error: Cannot modify thinking blocks from different model versions
- Interleaved thinking + multi-tool turn corrupts thinking block (text blanked, signature kept) → permanent 400 'blocks must remain as they were'
- [BUG] Mode/permission changes mid-tool-loop (effortLevel: xhigh) poisons entire session
- Session failure log: Opus 4.6 ignores its own rules for an entire session
- [BUG] "400 Guardrail was enabled" error when using Claude Opus 4.8 with AWS Bedrock
- [Feature Request] Add subagent approach selection option to avoid accidental feedback
- Persistent 400 'thinking blocks in the latest assistant message cannot be modified' — interleaved thinking persisted with empty text + signature bricks sessions
- [BUG] DesktopvsApp
- [BUG] Opus 4.7 cache hit rate collapse after May 27 incident — Messages 1.1k→88.9k in 9 minutes, $630/session
- [Bug] Anthropic API Error: Invalid thinking block format
- [BUG] FUCK CLAUDE
- Opus 4.8 extended thinking: Stop hook block re-entry corrupts thinking blocks → 400
- [Bug] 4.8 Fails when accessing previous model history
- [Bug] Unintended File Modifications During Execution
- [DOCS] Model configuration docs omit lean system prompt default scope and model exceptions
- Add "Always allow globally" option to permission prompts
- Server-side model upgrade (Opus 4.7→4.8) wedges in-flight sessions with `thinking blocks cannot be modified` 400
- [DOCS] AskUserQuestion docs missing multiple-choice prompt decision threshold
- [DOCS] Agent view docs omit shell-command background session launch syntax
- [DOCS] Agent view dispatch input docs incorrectly imply `/logout` dispatches as a prompt
- [DOCS] Claude in Chrome docs omit connected-browser selection behavior
- [DOCS] Plugin docs omit `defaultEnabled: false` for opt-in plugins
- Feature Request: Customizable chat text colors for user and assistant messages
- [DOCS] `/plugin` Discover tab docs omit directory-based suggested plugin pins
- VSCode Chrome integration silently fails: 3 distinct bugs
- [DOCS] MCP stdio docs omit session environment variables
- [Bug] Anthropic API error on second request within session with Claude Opus 4.8
- Cowork emits a blank session "index" handoff on focus when a CLI session is paused awaiting input
- [DOCS] MCP docs omit `claude mcp list/get` pending-approval output for unapproved project servers
- [BUG] /compact fails with 400 error when last assistant turn contains thinking blocks
- [DOCS] `/claude-api` docs omit Opus 4.8 migration guidance
- [DOCS] Fast mode docs still recommend deprecated Opus 4.6 override variable
- [DOCS] Bash tool docs omit `$TMPDIR` consistency across sandboxed and unsandboxed commands
- [Bug] Anthropic API Error: 400 Bad Request on Extended Thinking
- [DOCS] Background session docs omit worktree-isolation behavior for spawned subagents
- Built-in mechanistic self-verification of verifiable claims (symmetric to the auto permission gate)
- [DOCS] Worktree docs do not clarify `worktree.baseRef: "head"` inside linked worktrees
- [BUG] Excessive RAM usage with multiple parallel chats (~10 sessions → 30 GB memory pressure, macOS OOM)
- [DOCS] Managed MCP policy docs omit invalid `allowedMcpServers`/`deniedMcpServers` entry behavior
- [DOCS] Effort docs omit `CLAUDE_CODE_ALWAYS_ENABLE_EFFORT` unsupported-model behavior
- Regression (2.1.147–2.1.150?): resuming an extended-thinking session after a CC update/model-switch → unrecoverable 400, session bricked
- [DOCS] Windows updater docs omit `claude.exe` in-use recovery guidance
- [DOCS] VS Code auto mode docs still tie mode-picker visibility to bypass-permissions setting
- [DOCS] MCP docs omit `/mcp` tool list and detail rendering behavior
- [DOCS] Fine-grained tool streaming docs still describe provider opt-in behavior
- bypassPermissions: session startup reads flat pref, GUI toggle writes per-account pref — they never sync
- [BUG] Claude Desktop Code tab causes disk write limit violation — 8.5GB in 11 min, macOS kills app (M5, v1.9659.1)
- Ultrareview v2.1.96: docs describe /tasks command + claude ultrareview --json subcommand that don't exist; findings hard to read after completion
- I'd be happy to help create a GitHub issue title, but I don't see the error message in your message. Could you please share the specific error you're encountering? That way I can generate an accurate and descriptive issue title for you.
- [BUG] Claude in Chrome `file_upload` rejects all scheduled-task sessions with misleading error (real cause: INVALID_SESSION)
- Extended thinking: signed thinking block 'cannot be modified' (400) permanently wedges session
- RTL text support for Hebrew (and Arabic) in Claude Code
- [Bug] Random errors occurring across multiple operations