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A tutorial on using vLLM to efficiently serve LLM inference for AI agents, covering concepts like continuous batching and PagedAttention to address performance bottlenecks.
vLLM, open-sourced by UC Berkeley's Sky Computing Lab, is a high-performance LLM inference and serving library. Through PagedAttention and continuous batching, it dramatically improves throughput and reduces VRAM waste, while being compatible with the OpenAI API and various hardware.
This thread recommends three key papers for mastering LLM inference: PagedAttention, Sarathi-Serve, and SGLang, which introduce efficient memory management, chunked prefills, and structured generation techniques used in modern inference engines like vLLM and TensorRT-LLM.
This paper proposes Kara, a sliding-window KV cache compression method for efficient serving of reasoning LLMs, addressing limitations in existing compression techniques by using bidirectional attention and a Token2Chunk module. The method is integrated into the KvLLM inference framework built upon vLLM, improving output throughput while maintaining performance.
Michael Goin announced removing PagedAttention from vLLM, a significant change to the open-source LLM inference engine.
A detailed blog post explaining how vLLM works, including PagedAttention, KV cache management, and continuous batching for efficient LLM serving.
Explains why LLM inference is increasingly memory-bandwidth bound due to the KV cache scaling with context length and concurrent users, and how systems like vLLM and PagedAttention improve memory utilization.
vLLM 0.20.0rc1 releases with major throughput, quantization, speculative decoding, and multi-hardware support enhancements for scalable LLM serving.