vLLM EngineDeadError 七种死因完整诊断：从 CUDA OOM 到 NCCL 段错误，读懂堆栈追踪找到真凶“EngineCore encountered an issue. See stack trace (above) for the root cause.”——你在 vLLM 的日志里看到这句话时，引擎已经死了。但堆栈追踪在"上面"——你知道该看什么吗？一、EngineDeadError 的本质vLLM V1 引擎架构有一个核心进程叫 EngineCore。它负责模型推理的调度和执行。当 EngineCore 进程因任何原因退出时，APIServer 就会抛出：vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.关键：这个错误本身没有信息量。真正的根因在堆栈追踪里——但 vLLM 的堆栈追踪通常有 50-100 行，初学者根本不知道从哪看起。本文整理了 GitHub Issues 上 EngineDeadError 的 7 种最常见死因，教你从堆栈追踪反推根因。二、七种死因 → 堆栈特征 → 修复死因 1：CUDA Out of Memory（占比最高 ~35%）堆栈特征：(EngineCore pid=xxx) ERROR ... [core.py] File "gpu_model_runner.py", line ..., in execute_model File "gpu_model_runner.py", line ..., in _model_forward ... torch.cuda.OutOfMemoryError: CUDA out of memory. Tried to allocate XXX MiB. GPU 0 has X GiB total.识别关键：堆栈中包含torch.cuda.OutOfMemoryError，且Tried to allocate的数值接近GPU X has ... total。死因细分：子类型堆栈位置修复Warmup OOMsampler.py:_dummy_sampler_run降--max-num-seqsKV Cache OOMgpu_model_runner.py:_model_forward降--max-model-lenModel Weight OOMloader.py或__init__降--gpu-memory-utilization或用量化修复优先级：降max-num-seqs→ 降gpu-memory-utilization→--enforce-eager。死因 2：CUDA illegal memory access（~20%）堆栈特征：(EngineCore pid=xxx) ERROR ... [core.py] File "qwen3_5.py", line 726, in forward File "qwen3_next.py", line 1456, in gdn_a