New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LM...
TL;DR · AI 摘要
Andrew Ng 推出新课程，介绍开源框架 SGLang 如何通过计算缓存优化 LLM 和图像生成的推理效率。
核心要点
- SGLang 通过缓存共享上下文减少重复计算，显著降低推理成本
- 课程涵盖从零实现 KV 缓存和跨请求的 RadixAttention 扩展
- 支持文本与扩散模型图像生成的多 GPU 加速优化
Running LLMs in production is expensive, and much of that https://t.co/baiT6LKDYY" / X
Andrew Ng on X: "New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys @lmsysorg and RadixArk @radixark, and taught by Richard Chen @richardczl, a Member of Technical Staff at RadixArk. Running LLMs in production is expensive, and much of that https://t.co/baiT6LKDYY" / X
Don’t miss what’s happening
New course: Efficient Inference with SGLang: Text and Image Generation, built in partnership with LMSys
and RadixArk
, and taught by Richard Chen
, a Member of Technical Staff at RadixArk. Running LLMs in production is expensive, and much of that cost comes from redundant computation. This short course teaches you to eliminate that waste using SGLang, an open-source inference framework that caches computation already done and reuses it across future requests. When ten users share the same system prompt, SGLang processes it once, not ten times. The speedups compound quickly, especially when there's a lot of shared context across requests. Skills you'll gain: - Implement a KV cache from scratch to eliminate redundant computation within a single request - Scale caching across users and requests with RadixAttention, so shared context is only processed once - Accelerate image generation with diffusion models using SGLang's caching and multi-GPU parallelism Join and learn to make LLM inference faster and more cost-efficient at scale! https://deeplearning.ai/short-courses/efficient-inference-with-sglang-text-and-image-generation…
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