Your current environment

Collecting environment information... uv is set

    System Info

============================== OS : Ubuntu 22.04.5 LTS (aarch64) GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.3) 11.4.0 Clang version : Could not collect CMake version : Could not collect Libc version : glibc-2.35

============================== PyTorch Info

PyTorch version : 2.11.0+cu130 Is debug build : False CUDA used to build PyTorch : 13.0 ROCM used to build PyTorch : N/A XPU used to build PyTorch : N/A

============================== Python Environment

Python version : 3.12.13 (main, May 4 2026, 21:09:59) [Clang 22.1.3 ] (64-bit runtime) Python platform : Linux-6.8.0-1050-nvidia-64k-aarch64-with-glibc2.35

============================== CUDA / GPU Info

Is CUDA available : True CUDA runtime version : 12.9.86 CUDA_MODULE_LOADING set to : GPU models and configuration : GPU 0: NVIDIA GH200 480GB Nvidia driver version : 590.48.01 cuDNN version : Could not collect HIP runtime version : N/A MIOpen runtime version : N/A Is XNNPACK available : True

============================== CPU Info

Architecture: aarch64 CPU op-mode(s): 64-bit Byte Order: Little Endian CPU(s): 72 On-line CPU(s) list: 0-71 Vendor ID: ARM Model name: Neoverse-V2 Model: 0 Thread(s) per core: 1 Core(s) per socket: 72 Socket(s): 1 Stepping: r0p0 Frequency boost: disabled CPU max MHz: 3447.0000 CPU min MHz: 81.0000 BogoMIPS: 2000.00 Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma lrcpc dcpop sha3 sm3 sm4 asimddp sha512 sve asimdfhm dit uscat ilrcpc flagm ssbs sb paca pacg dcpodp sve2 sveaes svepmull svebitperm svesha3 svesm4 flagm2 frint svei8mm svebf16 i8mm bf16 dgh bti L1d cache: 4.5 MiB (72 instances) L1i cache: 4.5 MiB (72 instances) L2 cache: 72 MiB (72 instances) L3 cache: 114 MiB (1 instance) NUMA node(s): 9 NUMA node0 CPU(s): 0-71 NUMA node1 CPU(s):
NUMA node2 CPU(s):
NUMA node3 CPU(s):
NUMA node4 CPU(s):
NUMA node5 CPU(s):
NUMA node6 CPU(s):
NUMA node7 CPU(s):
NUMA node8 CPU(s):
Vulnerability Gather data sampling: Not affected Vulnerability Indirect target selection: Not affected Vulnerability Itlb multihit: Not affected Vulnerability L1tf: Not affected Vulnerability Mds: Not affected Vulnerability Meltdown: Not affected Vulnerability Mmio stale data: Not affected Vulnerability Reg file data sampling: Not affected Vulnerability Retbleed: Not affected Vulnerability Spec rstack overflow: Not affected Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl Vulnerability Spectre v1: Mitigation; __user pointer sanitization Vulnerability Spectre v2: Mitigation; CSV2, BHB Vulnerability Srbds: Not affected Vulnerability Tsa: Not affected Vulnerability Tsx async abort: Not affected Vulnerability Vmscape: Not affected

============================== Versions of relevant libraries

[pip3] flashinfer-python==0.6.8.post1 [pip3] numpy==2.3.5 [pip3] nvidia-cublas==13.1.0.3 [pip3] nvidia-cuda-cupti==13.0.85 [pip3] nvidia-cuda-nvrtc==13.0.88 [pip3] nvidia-cuda-runtime==13.0.96 [pip3] nvidia-cudnn-cu13==9.19.0.56 [pip3] nvidia-cudnn-frontend==1.18.0 [pip3] nvidia-cufft==12.0.0.61 [pip3] nvidia-cufile==1.15.1.6 [pip3] nvidia-curand==10.4.0.35 [pip3] nvidia-cusolver==12.0.4.66 [pip3] nvidia-cusparse==12.6.3.3 [pip3] nvidia-cusparselt-cu13==0.8.0 [pip3] nvidia-cutlass-dsl==4.4.2 [pip3] nvidia-cutlass-dsl-libs-base==4.4.2 [pip3] nvidia-ml-py==13.595.45 [pip3] nvidia-nccl-cu13==2.28.9 [pip3] nvidia-nvjitlink==13.0.88 [pip3] nvidia-nvshmem-cu13==3.4.5 [pip3] nvidia-nvtx==13.0.85 [pip3] pyzmq==27.1.0 [pip3] torch==2.11.0+cu130 [pip3] torch-c-dlpack-ext==0.1.5 [pip3] torchaudio==2.11.0+cu130 [pip3] torchvision==0.26.0+cu130 [pip3] transformers==5.7.0 [pip3] triton==3.6.0 [conda] Could not collect

============================== vLLM Info

ROCM Version : Could not collect vLLM Version : 0.20.1 vLLM Build Flags: CUDA Archs: Not Set; ROCm: Disabled; XPU: Disabled GPU Topology: GPU0 NIC0 NIC1 CPU Affinity NUMA Affinity GPU NUMA ID GPU0 X NODE NODE 0-71 0 1 NIC0 NODE X PIX NIC1 NODE PIX X

Legend:

X = Self SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI) NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU) PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge) PIX = Connection traversing at most a single PCIe bridge NV# = Connection traversing a bonded set of # NVLinks

NIC Legend:

NIC0: mlx5_0 NIC1: mlx5_1

============================== Environment Variables

CUDA_HOME=/usr/local/cuda CUDA_HOME=/usr/local/cuda PYTORCH_NVML_BASED_CUDA_CHECK=1 TORCHINDUCTOR_COMPILE_THREADS=1 TORCHINDUCTOR_CACHE_DIR=/tmp/torchinductor_mlin

Your output of `python collect_env.py` here

🐛 Describe the bug

Set up env like this:

uv venv --python 312
source .venv/bin/activate
uv pip install vllm --torch-backend=auto

then run this

SAFETENSORS_FAST_GPU=1 vllm serve \
    MiniMaxAI/MiniMax-M2.7 --trust-remote-code \
    --enable-auto-tool-choice --tool-call-parser minimax_m2 \
    --reasoning-parser minimax_m2_append_think \
    --gpu-memory-utilization 0.95 --cpu-offload-gb 300

get this error

(EngineCore pid=36444) ValueError: To serve at least one request with the models's max seq len (204800), (48.44 GiB KV cache is needed, which is larger than the available KV cache memory (24.85 GiB). Based on the available memory, the estimated maximum model length is 105056. Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine. See https://docs.vllm.ai/en/latest/configuration/conserving_memory/ for more details.
[rank0]:[W508 10:49:32.296904081 ProcessGroupNCCL.cpp:1575] Warning: WARNING: destroy_process_group() was not called before program exit, which can leak resources. For more info, please see https://pytorch.org/docs/stable/distributed.html#shutdown (function operator())
(APIServer pid=36253) Traceback (most recent call last):

afterwards, there is about 40 GB of GPU memory unfreed on the GPU when viewed via nvidia-smi. but there is no process attached to it. we have to reboot our server.

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