vllm - 💡(How to fix) Fix [Bug]:CUDA illegal instruction during decode (V1 Engine + NVFP4) on aarch64 (NVIDIA GB10) [3 comments, 2 participants]
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Xenon0220
ZJY0516Error Message
When running an NVFP4 quantized MoE model (qwen3.5-35b-a3b-nvfp4) on an ARM64 server equipped with an NVIDIA GB10 (Grace Blackwell) GPU (which natively supports NVFP4), the vLLM V1 engine crashes with a torch.AcceleratorError: CUDA error: an illegal instruction was encountered. (EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:72] Dumping input data for V1 LLM engine (v0.19.1rc1.dev203+g0f3ce4c74) with config: model='/workspace/models/qwen3.5-35b-a3b-nvfp4', speculative_config=None, tokenizer='/workspace/models/qwen3.5-35b-a3b-nvfp4', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=compressed-tensors, quantization_config=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='qwen3', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=0, served_model_name=qwen3.5-35b, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '/root/.cache/vllm/torch_compile_cache/617ec03b22', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'ir_enable_torch_wrap': True, 'splitting_ops': ['vllm::unified_attention_with_output', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::olmo_hybrid_gdn_full_forward', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::unified_kv_cache_update', 'vllm::unified_mla_kv_cache_update'], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_images_per_batch': 0, 'compile_sizes': [], 'compile_ranges_endpoints': [8192], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'size_asserts': False, 'alignment_asserts': False, 'scalar_asserts': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 320, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 512, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': '/root/.cache/vllm/torch_compile_cache/617ec03b22/rank_0_0/backbone', 'fast_moe_cold_start': False, 'static_all_moe_layers': ['language_model.model.layers.0.mlp.experts', 'language_model.model.layers.1.mlp.experts', 'language_model.model.layers.2.mlp.experts', 'language_model.model.layers.3.mlp.experts', 'language_model.model.layers.4.mlp.experts', 'language_model.model.layers.5.mlp.experts', 'language_model.model.layers.6.mlp.experts', 'language_model.model.layers.7.mlp.experts', 'language_model.model.layers.8.mlp.experts', 'language_model.model.layers.9.mlp.experts', 'language_model.model.layers.10.mlp.experts', 'language_model.model.layers.11.mlp.experts', 'language_model.model.layers.12.mlp.experts', 'language_model.model.layers.13.mlp.experts', 'language_model.model.layers.14.mlp.experts', 'language_model.model.layers.15.mlp.experts', 'language_model.model.layers.16.mlp.experts', 'language_model.model.layers.17.mlp.experts', 'language_model.model.layers.18.mlp.experts', 'language_model.model.layers.19.mlp.experts', 'language_model.model.layers.20.mlp.experts', 'language_model.model.layers.21.mlp.experts', 'language_model.model.layers.22.mlp.experts', 'language_model.model.layers.23.mlp.experts', 'language_model.model.layers.24.mlp.experts', 'language_model.model.layers.25.mlp.experts', 'language_model.model.layers.26.mlp.experts', 'language_model.model.layers.27.mlp.experts', 'language_model.model.layers.28.mlp.experts', 'language_model.model.layers.29.mlp.experts', 'language_model.model.layers.30.mlp.experts', 'language_model.model.layers.31.mlp.experts', 'language_model.model.layers.32.mlp.experts', 'language_model.model.layers.33.mlp.experts', 'language_model.model.layers.34.mlp.experts', 'language_model.model.layers.35.mlp.experts', 'language_model.model.layers.36.mlp.experts', 'language_model.model.layers.37.mlp.experts', 'language_model.model.layers.38.mlp.experts', 'language_model.model.layers.39.mlp.experts']}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native']), enable_flashinfer_autotune=True, moe_backend='auto'), (EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:79] Dumping scheduler output for model execution: SchedulerOutput(scheduled_new_reqs=[], scheduled_cached_reqs=CachedRequestData(req_ids=['chatcmpl-bcfbde213cb28031-bc849a28'],resumed_req_ids=set(),new_token_ids_lens=[],all_token_ids_lens={},new_block_ids=[([5], [6], [7], [8])],num_computed_tokens=[1056],num_output_tokens=[1043]), num_scheduled_tokens={chatcmpl-bcfbde213cb28031-bc849a28: 1}, total_num_scheduled_tokens=1, scheduled_spec_decode_tokens={}, scheduled_encoder_inputs={}, num_common_prefix_blocks=[0, 0, 0, 2], finished_req_ids=[], free_encoder_mm_hashes=[], preempted_req_ids=[], has_structured_output_requests=false, pending_structured_output_tokens=false, num_invalid_spec_tokens=null, kv_connector_metadata=null, ec_connector_metadata=null, new_block_ids_to_zero=[8]) (EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:81] Dumping scheduler stats: SchedulerStats(num_running_reqs=1, num_waiting_reqs=0, step_counter=0, current_wave=0, kv_cache_usage=0.0011814744801512678, encoder_cache_usage=0.0, prefix_cache_stats=PrefixCacheStats(reset=False, requests=0, queries=0, hits=0, preempted_requests=0, preempted_queries=0, preempted_hits=0), connector_prefix_cache_stats=None, kv_cache_eviction_events=[], spec_decoding_stats=None, kv_connector_stats=None, waiting_lora_adapters={}, running_lora_adapters={}, cudagraph_stats=None, perf_stats=None) (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] EngineCore encountered a fatal error. (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Traceback (most recent call last): (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1103, in run_engine_core (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] engine_core.run_busy_loop() (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1144, in run_busy_loop (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] self._process_engine_step() (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1183, in _process_engine_step (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] outputs, model_executed = self.step_fn() (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^ (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 501, in step_with_batch_queue (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] model_output = future.result() (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^ (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/_base.py", line 456, in result (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] return self.__get_result() (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^^^^^ (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] raise self._exception (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/thread.py", line 59, in run (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] result = self.fn(*self.args, **self.kwargs) (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 266, in get_output (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] self.async_copy_ready_event.synchronize() (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] torch.AcceleratorError: CUDA error: an illegal instruction was encountered (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Search for `cudaErrorIllegalInstruction' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information. (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] For debugging consider passing CUDA_LAUNCH_BLOCKING=1
Root Cause
(APIServer pid=1) INFO 04-14 02:41:48 [loggers.py:259] Engine 000: Avg prompt throughput: 1.4 tokens/s, Avg generation throughput: 9.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.1%, Prefix cache hit rate: 0.0%
(APIServer pid=1) INFO 04-14 02:41:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 36.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.1%, Prefix cache hit rate: 0.0%
(APIServer pid=1) INFO 04-14 02:42:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 37.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.1%, Prefix cache hit rate: 0.0%
(EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:72] Dumping input data for V1 LLM engine (v0.19.1rc1.dev203+g0f3ce4c74) with config: model='/workspace/models/qwen3.5-35b-a3b-nvfp4', speculative_config=None, tokenizer='/workspace/models/qwen3.5-35b-a3b-nvfp4', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=compressed-tensors, quantization_config=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='qwen3', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=0, served_model_name=qwen3.5-35b, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '/root/.cache/vllm/torch_compile_cache/617ec03b22', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'ir_enable_torch_wrap': True, 'splitting_ops': ['vllm::unified_attention_with_output', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::olmo_hybrid_gdn_full_forward', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::unified_kv_cache_update', 'vllm::unified_mla_kv_cache_update'], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_images_per_batch': 0, 'compile_sizes': [], 'compile_ranges_endpoints': [8192], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'size_asserts': False, 'alignment_asserts': False, 'scalar_asserts': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 320, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 512, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': '/root/.cache/vllm/torch_compile_cache/617ec03b22/rank_0_0/backbone', 'fast_moe_cold_start': False, 'static_all_moe_layers': ['language_model.model.layers.0.mlp.experts', 'language_model.model.layers.1.mlp.experts', 'language_model.model.layers.2.mlp.experts', 'language_model.model.layers.3.mlp.experts', 'language_model.model.layers.4.mlp.experts', 'language_model.model.layers.5.mlp.experts', 'language_model.model.layers.6.mlp.experts', 'language_model.model.layers.7.mlp.experts', 'language_model.model.layers.8.mlp.experts', 'language_model.model.layers.9.mlp.experts', 'language_model.model.layers.10.mlp.experts', 'language_model.model.layers.11.mlp.experts', 'language_model.model.layers.12.mlp.experts', 'language_model.model.layers.13.mlp.experts', 'language_model.model.layers.14.mlp.experts', 'language_model.model.layers.15.mlp.experts', 'language_model.model.layers.16.mlp.experts', 'language_model.model.layers.17.mlp.experts', 'language_model.model.layers.18.mlp.experts', 'language_model.model.layers.19.mlp.experts', 'language_model.model.layers.20.mlp.experts', 'language_model.model.layers.21.mlp.experts', 'language_model.model.layers.22.mlp.experts', 'language_model.model.layers.23.mlp.experts', 'language_model.model.layers.24.mlp.experts', 'language_model.model.layers.25.mlp.experts', 'language_model.model.layers.26.mlp.experts', 'language_model.model.layers.27.mlp.experts', 'language_model.model.layers.28.mlp.experts', 'language_model.model.layers.29.mlp.experts', 'language_model.model.layers.30.mlp.experts', 'language_model.model.layers.31.mlp.experts', 'language_model.model.layers.32.mlp.experts', 'language_model.model.layers.33.mlp.experts', 'language_model.model.layers.34.mlp.experts', 'language_model.model.layers.35.mlp.experts', 'language_model.model.layers.36.mlp.experts', 'language_model.model.layers.37.mlp.experts', 'language_model.model.layers.38.mlp.experts', 'language_model.model.layers.39.mlp.experts']}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native']), enable_flashinfer_autotune=True, moe_backend='auto'),
(EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:79] Dumping scheduler output for model execution: SchedulerOutput(scheduled_new_reqs=[], scheduled_cached_reqs=CachedRequestData(req_ids=['chatcmpl-bcfbde213cb28031-bc849a28'],resumed_req_ids=set(),new_token_ids_lens=[],all_token_ids_lens={},new_block_ids=[([5], [6], [7], [8])],num_computed_tokens=[1056],num_output_tokens=[1043]), num_scheduled_tokens={chatcmpl-bcfbde213cb28031-bc849a28: 1}, total_num_scheduled_tokens=1, scheduled_spec_decode_tokens={}, scheduled_encoder_inputs={}, num_common_prefix_blocks=[0, 0, 0, 2], finished_req_ids=[], free_encoder_mm_hashes=[], preempted_req_ids=[], has_structured_output_requests=false, pending_structured_output_tokens=false, num_invalid_spec_tokens=null, kv_connector_metadata=null, ec_connector_metadata=null, new_block_ids_to_zero=[8])
(EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:81] Dumping scheduler stats: SchedulerStats(num_running_reqs=1, num_waiting_reqs=0, step_counter=0, current_wave=0, kv_cache_usage=0.0011814744801512678, encoder_cache_usage=0.0, prefix_cache_stats=PrefixCacheStats(reset=False, requests=0, queries=0, hits=0, preempted_requests=0, preempted_queries=0, preempted_hits=0), connector_prefix_cache_stats=None, kv_cache_eviction_events=[], spec_decoding_stats=None, kv_connector_stats=None, waiting_lora_adapters={}, running_lora_adapters={}, cudagraph_stats=None, perf_stats=None)
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] EngineCore encountered a fatal error.
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Traceback (most recent call last):
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1103, in run_engine_core
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] engine_core.run_busy_loop()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1144, in run_busy_loop
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] self._process_engine_step()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1183, in _process_engine_step
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] outputs, model_executed = self.step_fn()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 501, in step_with_batch_queue
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] model_output = future.result()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/_base.py", line 456, in result
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] return self.__get_result()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] raise self._exception
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/thread.py", line 59, in run
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] result = self.fn(*self.args, **self.kwargs)
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 266, in get_output
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] self.async_copy_ready_event.synchronize()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] torch.AcceleratorError: CUDA error: an illegal instruction was encountered
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Search for cudaErrorIllegalInstruction' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information. (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] For debugging consider passing CUDA_LAUNCH_BLOCKING=1 (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Compile with TORCH_USE_CUDA_DSAto enable device-side assertions. (EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] (EngineCore pid=166) Process EngineCore: (EngineCore pid=166) Traceback (most recent call last): (EngineCore pid=166) File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap (EngineCore pid=166) self.run() (EngineCore pid=166) File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run (EngineCore pid=166) self._target(*self._args, **self._kwargs) (EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1114, in run_engine_core (EngineCore pid=166) raise e (EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1103, in run_engine_core (EngineCore pid=166) engine_core.run_busy_loop() (EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1144, in run_busy_loop (EngineCore pid=166) self._process_engine_step() (EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1183, in _process_engine_step (EngineCore pid=166) outputs, model_executed = self.step_fn() (EngineCore pid=166) ^^^^^^^^^^^^^^ (EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 501, in step_with_batch_queue (EngineCore pid=166) model_output = future.result() (EngineCore pid=166) ^^^^^^^^^^^^^^^ (EngineCore pid=166) File "/usr/lib/python3.12/concurrent/futures/_base.py", line 456, in result (EngineCore pid=166) return self.__get_result() (EngineCore pid=166) ^^^^^^^^^^^^^^^^^^^ (EngineCore pid=166) File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result (EngineCore pid=166) raise self._exception (EngineCore pid=166) File "/usr/lib/python3.12/concurrent/futures/thread.py", line 59, in run (EngineCore pid=166) result = self.fn(*self.args, **self.kwargs) (EngineCore pid=166) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ (EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 266, in get_output (EngineCore pid=166) self.async_copy_ready_event.synchronize() (EngineCore pid=166) torch.AcceleratorError: CUDA error: an illegal instruction was encountered (EngineCore pid=166) Search forcudaErrorIllegalInstruction' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
(EngineCore pid=166) CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(EngineCore pid=166) For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(EngineCore pid=166) Compile with TORCH_USE_CUDA_DSA to enable device-side assertions.
(EngineCore pid=166)
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] AsyncLLM output_handler failed.
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] Traceback (most recent call last):
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 657, in output_handler
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] outputs = await engine_core.get_output_async()
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 998, in get_output_async
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] raise self._format_exception(outputs) from None
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] Error in chat completion stream generator.
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] Traceback (most recent call last):
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 586, in chat_completion_stream_generator
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] async for res in result_generator:
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 576, in generate
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] out = q.get_nowait() or await q.get()
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] ^^^^^^^^^^^^^
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/output_processor.py", line 85, in get
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] raise output
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 657, in output_handler
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] outputs = await engine_core.get_output_async()
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 998, in get_output_async
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] raise self._format_exception(outputs) from None
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
[rank0]:[W414 02:42:14.851794153 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=1) INFO 04-14 02:42:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 21.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.2%, Prefix cache hit rate: 0.0%
(APIServer pid=1) INFO: Shutting down
(APIServer pid=1) INFO: Waiting for application shutdown.
(APIServer pid=1) INFO: Application shutdown complete.
(APIServer pid=1) INFO: Finished server process [1]
Fix Action
Fix / Workaround
Workaround: Appending --enforce-eager to the docker run command bypasses the crash entirely, which further confirms it is a compilation issue rather than a hardware limitation.
RAW_BUFFERClick to expand / collapse
Your current environment
<details> <summary>The output of <code>python collect_env.py</code></summary># I couldn't run collect_env.py easily due to the crash, but here is my exact environment based on nvidia-smi and the docker image logs:
- OS: Linux (aarch64)
- GPU: NVIDIA GB10 (Grace Blackwell)
- CUDA Version: 13.0
- Driver Version: 580.126.09
- vLLM Version: v0.19.1rc1.dev203+g0f3ce4c74
- Docker Image: vllm/vllm-openai:cu130-nightly-aarch64
</details>
### 🐛 Describe the bug
🐛 Describe the bug
When running an NVFP4 quantized MoE model (qwen3.5-35b-a3b-nvfp4) on an ARM64 server equipped with an NVIDIA GB10 (Grace Blackwell) GPU (which natively supports NVFP4), the vLLM V1 engine crashes with a torch.AcceleratorError: CUDA error: an illegal instruction was encountered.
The prefill phase successfully processes tokens (in my case, processing 57,260 prompt tokens with an 84% prefix cache hit rate), but the engine crashes specifically during the decode phase (around output token 45) inside GPUModelRunner.get_output().
Since the Blackwell hardware natively supports NVFP4 instructions, this strongly suggests a JIT compilation bug (torch.compile / Inductor) or CUDA graph issue generating invalid PTX/SASS instructions for aarch64 under the V1 Engine.
Workaround:
Appending --enforce-eager to the docker run command bypasses the crash entirely, which further confirms it is a compilation issue rather than a hardware limitation.
To Reproduce
Run the following command on a GB10 (aarch64) node:
Bash
docker run --runtime nvidia --gpus all \
-v /home/admin/models:/workspace/models \
-p 8081:8000 \
--ipc=host \
vllm/vllm-openai:cu130-nightly-aarch64 \
--model /workspace/models/qwen3.5-35b-a3b-nvfp4 \
--served-model-name qwen3.5-35b \
--tensor-parallel-size 1 \
--max-model-len 131072 \
--enable-chunked-prefill \
--enable-prefix-caching \
--max-num-batched-tokens 16384 \
--gpu-memory-utilization 0.9 \
--reasoning-parser qwen3 \
--tool-call-parser qwen3_xml \
--enable-auto-tool-choice \
--language-model-only
Relevant log output
(APIServer pid=1) INFO 04-14 02:41:48 [loggers.py:259] Engine 000: Avg prompt throughput: 1.4 tokens/s, Avg generation throughput: 9.3 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.1%, Prefix cache hit rate: 0.0%
(APIServer pid=1) INFO 04-14 02:41:58 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 36.6 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.1%, Prefix cache hit rate: 0.0%
(APIServer pid=1) INFO 04-14 02:42:08 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 37.0 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.1%, Prefix cache hit rate: 0.0%
(EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:72] Dumping input data for V1 LLM engine (v0.19.1rc1.dev203+g0f3ce4c74) with config: model='/workspace/models/qwen3.5-35b-a3b-nvfp4', speculative_config=None, tokenizer='/workspace/models/qwen3.5-35b-a3b-nvfp4', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, decode_context_parallel_size=1, dcp_comm_backend=ag_rs, disable_custom_all_reduce=False, quantization=compressed-tensors, quantization_config=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='qwen3', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False, enable_mfu_metrics=False, enable_mm_processor_stats=False, enable_logging_iteration_details=False), seed=0, served_model_name=qwen3.5-35b, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.VLLM_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '/root/.cache/vllm/torch_compile_cache/617ec03b22', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'ir_enable_torch_wrap': True, 'splitting_ops': ['vllm::unified_attention_with_output', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::olmo_hybrid_gdn_full_forward', 'vllm::kda_attention', 'vllm::sparse_attn_indexer', 'vllm::rocm_aiter_sparse_attn_indexer', 'vllm::unified_kv_cache_update', 'vllm::unified_mla_kv_cache_update'], 'compile_mm_encoder': False, 'cudagraph_mm_encoder': False, 'encoder_cudagraph_token_budgets': [], 'encoder_cudagraph_max_images_per_batch': 0, 'compile_sizes': [], 'compile_ranges_endpoints': [8192], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'size_asserts': False, 'alignment_asserts': False, 'scalar_asserts': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': <CUDAGraphMode.FULL_AND_PIECEWISE: (2, 1)>, 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 320, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': True, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 512, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': '/root/.cache/vllm/torch_compile_cache/617ec03b22/rank_0_0/backbone', 'fast_moe_cold_start': False, 'static_all_moe_layers': ['language_model.model.layers.0.mlp.experts', 'language_model.model.layers.1.mlp.experts', 'language_model.model.layers.2.mlp.experts', 'language_model.model.layers.3.mlp.experts', 'language_model.model.layers.4.mlp.experts', 'language_model.model.layers.5.mlp.experts', 'language_model.model.layers.6.mlp.experts', 'language_model.model.layers.7.mlp.experts', 'language_model.model.layers.8.mlp.experts', 'language_model.model.layers.9.mlp.experts', 'language_model.model.layers.10.mlp.experts', 'language_model.model.layers.11.mlp.experts', 'language_model.model.layers.12.mlp.experts', 'language_model.model.layers.13.mlp.experts', 'language_model.model.layers.14.mlp.experts', 'language_model.model.layers.15.mlp.experts', 'language_model.model.layers.16.mlp.experts', 'language_model.model.layers.17.mlp.experts', 'language_model.model.layers.18.mlp.experts', 'language_model.model.layers.19.mlp.experts', 'language_model.model.layers.20.mlp.experts', 'language_model.model.layers.21.mlp.experts', 'language_model.model.layers.22.mlp.experts', 'language_model.model.layers.23.mlp.experts', 'language_model.model.layers.24.mlp.experts', 'language_model.model.layers.25.mlp.experts', 'language_model.model.layers.26.mlp.experts', 'language_model.model.layers.27.mlp.experts', 'language_model.model.layers.28.mlp.experts', 'language_model.model.layers.29.mlp.experts', 'language_model.model.layers.30.mlp.experts', 'language_model.model.layers.31.mlp.experts', 'language_model.model.layers.32.mlp.experts', 'language_model.model.layers.33.mlp.experts', 'language_model.model.layers.34.mlp.experts', 'language_model.model.layers.35.mlp.experts', 'language_model.model.layers.36.mlp.experts', 'language_model.model.layers.37.mlp.experts', 'language_model.model.layers.38.mlp.experts', 'language_model.model.layers.39.mlp.experts']}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native']), enable_flashinfer_autotune=True, moe_backend='auto'),
(EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:79] Dumping scheduler output for model execution: SchedulerOutput(scheduled_new_reqs=[], scheduled_cached_reqs=CachedRequestData(req_ids=['chatcmpl-bcfbde213cb28031-bc849a28'],resumed_req_ids=set(),new_token_ids_lens=[],all_token_ids_lens={},new_block_ids=[([5], [6], [7], [8])],num_computed_tokens=[1056],num_output_tokens=[1043]), num_scheduled_tokens={chatcmpl-bcfbde213cb28031-bc849a28: 1}, total_num_scheduled_tokens=1, scheduled_spec_decode_tokens={}, scheduled_encoder_inputs={}, num_common_prefix_blocks=[0, 0, 0, 2], finished_req_ids=[], free_encoder_mm_hashes=[], preempted_req_ids=[], has_structured_output_requests=false, pending_structured_output_tokens=false, num_invalid_spec_tokens=null, kv_connector_metadata=null, ec_connector_metadata=null, new_block_ids_to_zero=[8])
(EngineCore pid=166) ERROR 04-14 02:42:14 [dump_input.py:81] Dumping scheduler stats: SchedulerStats(num_running_reqs=1, num_waiting_reqs=0, step_counter=0, current_wave=0, kv_cache_usage=0.0011814744801512678, encoder_cache_usage=0.0, prefix_cache_stats=PrefixCacheStats(reset=False, requests=0, queries=0, hits=0, preempted_requests=0, preempted_queries=0, preempted_hits=0), connector_prefix_cache_stats=None, kv_cache_eviction_events=[], spec_decoding_stats=None, kv_connector_stats=None, waiting_lora_adapters={}, running_lora_adapters={}, cudagraph_stats=None, perf_stats=None)
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] EngineCore encountered a fatal error.
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Traceback (most recent call last):
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1103, in run_engine_core
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] engine_core.run_busy_loop()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1144, in run_busy_loop
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] self._process_engine_step()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1183, in _process_engine_step
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] outputs, model_executed = self.step_fn()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 501, in step_with_batch_queue
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] model_output = future.result()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/_base.py", line 456, in result
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] return self.__get_result()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] raise self._exception
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/lib/python3.12/concurrent/futures/thread.py", line 59, in run
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] result = self.fn(*self.args, **self.kwargs)
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 266, in get_output
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] self.async_copy_ready_event.synchronize()
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] torch.AcceleratorError: CUDA error: an illegal instruction was encountered
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Search for `cudaErrorIllegalInstruction' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112] Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
(EngineCore pid=166) ERROR 04-14 02:42:14 [core.py:1112]
(EngineCore pid=166) Process EngineCore:
(EngineCore pid=166) Traceback (most recent call last):
(EngineCore pid=166) File "/usr/lib/python3.12/multiprocessing/process.py", line 314, in _bootstrap
(EngineCore pid=166) self.run()
(EngineCore pid=166) File "/usr/lib/python3.12/multiprocessing/process.py", line 108, in run
(EngineCore pid=166) self._target(*self._args, **self._kwargs)
(EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1114, in run_engine_core
(EngineCore pid=166) raise e
(EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1103, in run_engine_core
(EngineCore pid=166) engine_core.run_busy_loop()
(EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1144, in run_busy_loop
(EngineCore pid=166) self._process_engine_step()
(EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 1183, in _process_engine_step
(EngineCore pid=166) outputs, model_executed = self.step_fn()
(EngineCore pid=166) ^^^^^^^^^^^^^^
(EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py", line 501, in step_with_batch_queue
(EngineCore pid=166) model_output = future.result()
(EngineCore pid=166) ^^^^^^^^^^^^^^^
(EngineCore pid=166) File "/usr/lib/python3.12/concurrent/futures/_base.py", line 456, in result
(EngineCore pid=166) return self.__get_result()
(EngineCore pid=166) ^^^^^^^^^^^^^^^^^^^
(EngineCore pid=166) File "/usr/lib/python3.12/concurrent/futures/_base.py", line 401, in __get_result
(EngineCore pid=166) raise self._exception
(EngineCore pid=166) File "/usr/lib/python3.12/concurrent/futures/thread.py", line 59, in run
(EngineCore pid=166) result = self.fn(*self.args, **self.kwargs)
(EngineCore pid=166) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(EngineCore pid=166) File "/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py", line 266, in get_output
(EngineCore pid=166) self.async_copy_ready_event.synchronize()
(EngineCore pid=166) torch.AcceleratorError: CUDA error: an illegal instruction was encountered
(EngineCore pid=166) Search for `cudaErrorIllegalInstruction' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.
(EngineCore pid=166) CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.
(EngineCore pid=166) For debugging consider passing CUDA_LAUNCH_BLOCKING=1
(EngineCore pid=166) Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.
(EngineCore pid=166)
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] AsyncLLM output_handler failed.
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] Traceback (most recent call last):
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 657, in output_handler
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] outputs = await engine_core.get_output_async()
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 998, in get_output_async
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] raise self._format_exception(outputs) from None
(APIServer pid=1) ERROR 04-14 02:42:14 [async_llm.py:701] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] Error in chat completion stream generator.
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] Traceback (most recent call last):
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/chat_completion/serving.py", line 586, in chat_completion_stream_generator
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] async for res in result_generator:
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 576, in generate
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] out = q.get_nowait() or await q.get()
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] ^^^^^^^^^^^^^
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/output_processor.py", line 85, in get
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] raise output
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/async_llm.py", line 657, in output_handler
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] outputs = await engine_core.get_output_async()
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 998, in get_output_async
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] raise self._format_exception(outputs) from None
(APIServer pid=1) ERROR 04-14 02:42:14 [serving.py:1262] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.
[rank0]:[W414 02:42:14.851794153 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=1) INFO 04-14 02:42:18 [loggers.py:259] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 21.4 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.2%, Prefix cache hit rate: 0.0%
(APIServer pid=1) INFO: Shutting down
(APIServer pid=1) INFO: Waiting for application shutdown.
(APIServer pid=1) INFO: Application shutdown complete.
(APIServer pid=1) INFO: Finished server process [1]
### Before submitting a new issue...
- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.extent analysis
TL;DR
The most likely fix is to append the --enforce-eager flag to the docker run command to bypass the compilation issue.
Guidance
- The error message
torch.AcceleratorError: CUDA error: an illegal instruction was encounteredsuggests a compilation issue with the CUDA kernel. - The fact that appending
--enforce-eagerto the docker run command bypasses the crash confirms that it is a compilation issue rather than a hardware limitation. - To further debug the issue, consider passing
CUDA_LAUNCH_BLOCKING=1to enable synchronous CUDA kernel launches, which can help identify the exact kernel causing the error. - Compile with
TORCH_USE_CUDA_DSAto enable device-side assertions, which can provide more detailed error messages.
Example
No code snippet is provided as the issue is related to a specific hardware and software configuration.
Notes
The issue is specific to the NVIDIA GB10 (Grace Blackwell) GPU and the vLLM V1 engine, and may not be reproducible on other hardware or software configurations.
Recommendation
Apply the workaround by appending --enforce-eager to the docker run command, as it has been confirmed to bypass the crash. This will allow the model to run, but may not provide the optimal performance. Further debugging and investigation are needed to resolve the underlying compilation issue.
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- [redacted at user request]
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- You've hit your weekly limit · resets May 31 at 5pm (Asia/Shanghai)
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- [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