vLLM

What it does

vLLM is an inference engine for serving language models. It combines memory-aware KV-cache management with continuous batching and exposes an API compatible with common OpenAI clients.

Why it’s interesting

Serving throughput depends on scheduling and GPU memory, not only model size. Its block management and batching strategies illustrate those tradeoffs.

Real-world use case

Serve a small instruction model on a compatible GPU host and connect an existing OpenAI SDK client to the local endpoint.

What I can learn

• 01

  Paged KV-cache allocation

  See how token KV-cache blocks are allocated on demand, reducing fragmentation in GPU memory and allowing more sequences to run concurrently.

• 02

  Continuous request batching

  Understand how requests are interleaved at each generation step so completed sequences free capacity for waiting work sooner.

• 03

  GPU kernels and memory transfer bottlenecks

  Trace how specialized GPU kernels move and compute attention data, and where memory bandwidth can limit inference throughput.

One thing to explore

KV-cache and scheduler implementation

Follow the vllm/v1/core implementation and connect it to the concepts above.