Efficient Scaled Serving Of vLLM Requests

vLLM separates HTTP handling and tokenization from GPU inference using AsyncLLM and EngineCore.

A continuous batching scheduler groups tokens from multiple requests into each forward pass to maximize GPU utilization.

The KVCacheManager stores transformer attention keys and values in fixed‐size GPU memory blocks for efficient reuse.

ModelRunners execute combined token batches through all transformer layers on GPUs using SIMD parallelism.

Generated tokens are streamed back to clients by the AsyncLLM and the API server in real time.