vLLM is the production-grade open-source inference server. PagedAttention for efficient KV cache management, continuous batching, tensor parallelism, and OpenAI-compatible API. What you deploy when you need to serve local models to many users.
- 01State what PagedAttention solves.
- 02Explain continuous batching.
- 03Recognize where vLLM is the right tool.
PagedAttention
vLLM's headline innovation. Manages KV cache like virtual memory: fixed-size blocks, allocated on demand, released when requests complete. Eliminates fragmentation that had bottlenecked prior serving stacks. 2-4x throughput improvement over naive KV management.
Continuous batching
Requests join and leave batches at each decode step rather than waiting for the batch to complete. Latency remains reasonable while throughput approaches peak. The combination of PagedAttention + continuous batching is what makes vLLM production-viable.
- vLLM is the production-grade open-source server.
- PagedAttention + continuous batching is the winning combination.
- Kwon et al., 'Efficient Memory Management for Large Language Model Serving with PagedAttention' (SOSP 2023)