Implementing Flash Attention Backend in SGLang: Basics and KV Cache

This blog post details the end-to-end implementation of the Flash Attention backend in SGLang, now the default attention backend in SGLang 0.4.6. It dives deep into how Attention Backends function in modern LLM serving engines and explains the inner workings of Flash Attention. The author shares implementation details, including KV cache and CUDA Graph support, and outlines future work such as Speculative Decoding, MLA, Llama 4, and multimodal support. Benchmarks show FA3 consistently delivers the highest throughput, outperforming FlashInfer and Triton.