Optimizing LLMs With SGLang: Efficient Deployment And Inference
SGLang optimizes Large Language Model (LLM) execution & deployment with RadixAttention, quantization & optimized CPU/GPU usage. Used in production by ByteDance & xAI, it's an open-source framework under active development.
Disclaimer: this is a report generated with my tool: https://github.com/DTeam-Top/tsw-cli. See it as an experiment not a formal research, 😄。 Summary SGLang is an open-source framework designed to optimize the execution and deployment of Large Language Models (LLMs). It addresses the computational demands and latency challenges associated with LLMs through various techniques like RadixAttention, quantization, and optimized CPU/GPU usage. SGLang features a Python-based DSL frontend and a highly optimized backend, enabling fast inference and structured output generation. It's current...