Efficient vLLM Inference Request Lifecycle

vLLM handles inference requests via an OpenAI-compatible API server that passes tokenized prompts to an AsyncLLM engine using asynchronous IPC.

EngineCore schedules and batches tokens from multiple requests with a continuous batching algorithm and manages KV cache blocks via a KVCacheManager.

ModelRunner processes batched tokens on GPUs in parallel using optimized FlashAttention and replayable CUDA graphs.

AsyncLLM detokenizes generated tokens and streams or returns them to clients through the API server.

The architecture separates CPU-bound tasks (tokenization, HTTP) from GPU-bound model execution to maximize throughput and avoid Python GIL issues.