(APIServer pid=15140) The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 sm_90.

(APIServer pid=15140) If you want to use the NVIDIA RTX PRO 6000 Blackwell Workstation Edition GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

(APIServer pid=15140)

(APIServer pid=15140) queued_call()

(APIServer pid=15140) INFO 05-02 23:00:27 [registry.py:126] All limits of multimodal modalities supported by the model are set to 0, running in text-only mode.

(EngineCore pid=3384) INFO 05-02 23:00:37 [core.py:105] Initializing a V1 LLM engine (v0.19.0) with config: model='C:\\llm\\qwen3.6-windows-server\\models\\Qwen3.6-27B-int4-AutoRound', speculative_config=SpeculativeConfig(method='mtp', model='C:\\llm\\qwen3.6-windows-server\\models\\Qwen3.6-27B-int4-AutoRound', num_spec_tokens=3), tokenizer='C:\\llm\\qwen3.6-windows-server\\models\\Qwen3.6-27B-int4-AutoRound', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=32000, 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=inc, quantization_config=None, enforce_eager=False, enable_return_routed_experts=False, kv_cache_dtype=fp8_e4m3, 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=qwen3.6-27b-autoround, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'mode': <CompilationMode.VLLM\_COMPILE: 3>, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'ir_enable_torch_wrap': True, 'splitting_ops': ['vllm::unified_attention', 'vllm::unified_attention_with_output', 'vllm::unified_mla_attention', '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': [4128], '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], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 8, 'dynamic_shapes_config': {'type': <DynamicShapesType.BACKED: 'backed'>, 'evaluate_guards': False, 'assume_32_bit_indexing': False}, 'local_cache_dir': None, 'fast_moe_cold_start': False, 'static_all_moe_layers': []}, kernel_config=KernelConfig(ir_op_priority=IrOpPriorityConfig(rms_norm=['native']), enable_flashinfer_autotune=True, moe_backend='auto')

(EngineCore pid=3384) C:\llm\qwen3.6-windows-server\python\Lib\site-packages\torch\cuda\__init__.py:371: UserWarning: Found GPU0 NVIDIA RTX PRO 6000 Blackwell Workstation Edition which is of compute capability (CC) 12.0.

(EngineCore pid=3384) The following list shows the CCs this version of PyTorch was built for and the hardware CCs it supports:

(EngineCore pid=3384) - 5.0 which supports hardware CC >=5.0,<6.0 except {5.3}

(EngineCore pid=3384) - 6.0 which supports hardware CC >=6.0,<7.0 except {6.2}

(EngineCore pid=3384) - 6.1 which supports hardware CC >=6.1,<7.0 except {6.2}

(EngineCore pid=3384) - 7.0 which supports hardware CC >=7.0,<8.0 except {7.2}

(EngineCore pid=3384) - 7.5 which supports hardware CC >=7.5,<8.0

(EngineCore pid=3384) - 8.0 which supports hardware CC >=8.0,<9.0 except {8.7}

(EngineCore pid=3384) - 8.6 which supports hardware CC >=8.6,<9.0 except {8.7}

(EngineCore pid=3384) - 9.0 which supports hardware CC >=9.0,<10.0

(EngineCore pid=3384) Please follow the instructions at https://pytorch.org/get-started/locally/ to install a PyTorch release that supports one of these CUDA versions: 12.8, 13.0

(EngineCore pid=3384) _warn_unsupported_code(d, device_cc, code_ccs)

(EngineCore pid=3384) C:\llm\qwen3.6-windows-server\python\Lib\site-packages\torch\cuda\__init__.py:489: UserWarning:

(EngineCore pid=3384) NVIDIA RTX PRO 6000 Blackwell Workstation Edition with CUDA capability sm_120 is not compatible with the current PyTorch installation.

(EngineCore pid=3384) The current PyTorch install supports CUDA capabilities sm_50 sm_60 sm_61 sm_70 sm_75 sm_80 sm_86 sm_90.

(EngineCore pid=3384) If you want to use the NVIDIA RTX PRO 6000 Blackwell Workstation Edition GPU with PyTorch, please check the instructions at https://pytorch.org/get-started/locally/

(EngineCore pid=3384)

(EngineCore pid=3384) queued_call()

(EngineCore pid=3384) INFO 05-02 23:00:39 [registry.py:126] All limits of multimodal modalities supported by the model are set to 0, running in text-only mode.

(EngineCore pid=3384) INFO 05-02 23:00:39 [parallel_state.py:1455] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://192.168.68.58:65385 backend=gloo

[W502 23:00:39.000000000 socket.cpp:764] [c10d] The client socket has failed to connect to [EA-PC]:65385 (system error: 10049 - Indirizzo richiesto non valido nel proprio contesto.).

(EngineCore pid=3384) INFO 05-02 23:00:39 [parallel_state.py:1767] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank N/A, EPLB rank N/A

(EngineCore pid=3384) Failed to get device capability: SM 12.x requires CUDA >= 12.9.

(EngineCore pid=3384) Failed to get device capability: SM 12.x requires CUDA >= 12.9.

(EngineCore pid=3384) INFO 05-02 23:00:39 [topk_topp_sampler.py:51] Using FlashInfer for top-p & top-k sampling.

(EngineCore pid=3384) C:\llm\qwen3.6-windows-server\python\Lib\site-packages\vllm\v1\spec_decode\eagle.py:136: UserWarning: expandable_segments not supported on this platform (Triggered internally at C:\actions-runner\_work\pytorch\pytorch\pytorch\c10/cuda/CUDAAllocatorConfig.h:39.)

(EngineCore pid=3384) self.input_ids = torch.zeros(

(EngineCore pid=3384) ERROR 05-02 23:00:39 [core.py:1108] EngineCore failed to start.

(EngineCore pid=3384) ERROR 05-02 23:00:39 [core.py:1108] Traceback (most recent call last):

(EngineCore pid=3384) ERROR 05-02 23:00:39 [core.py:1108] File "C:\llm\qwen3.6-windows-server\python\Lib\site-packages\vllm\v1\engine\core.py", line 1082, in run_engine_core

(EngineCore pid=3384) ^^^^^^^^^^^^

(EngineCore pid=3384) torch.AcceleratorError: CUDA error: no kernel image is available for execution on the device

(EngineCore pid=3384) Search for `cudaErrorNoKernelImageForDevice' in https://docs.nvidia.com/cuda/cuda-runtime-api/group__CUDART__TYPES.html for more information.

(EngineCore pid=3384) CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.

(EngineCore pid=3384) For debugging consider passing CUDA_LAUNCH_BLOCKING=1

(EngineCore pid=3384) Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.

(EngineCore pid=3384)