Boost LLM performance: New SGLang course is live 🚀

Learn more: https://bit.ly/4du2u69 Introducing 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. Much of that cost comes from redundant computation: every new request forces the model to reprocess the same system prompt and shared context from scratch. SGLang is an open-source inference framework that eliminates that waste by caching computation that's already been done and reusing it across future requests. In this course, you'll build a clear mental model of how inference works (from input tokens to generated output) and learn why the memory bottleneck exists. From there, you'll implement the KV cache from scratch to store and reuse intermediate attention values within a single request. Then you'll go further with RadixAttention, SGLang's approach to sharing KV cache across requests by identifying common prefixes using a radix tree. Finally, you'll apply these same optimization principles to image generation using diffusion models. In detail, you'll: - Build a mental model of LLM inference: how a model processes input tokens, generates output token by token, and where the computational cost accumulates. - Implement the attention mechanism from scratch and build a KV cache to store and reuse intermediate key-value tensors, cutting redundant computation within a single request. - Extend caching across requests using SGLang's RadixAttention, which uses a radix tree to identify shared prefixes across users and skip repeated processing. - Apply SGLang's caching strategies to diffusion models for faster image generation, and explore multi-GPU parallelism for further acceleration. - Survey where the inference field is heading, including emerging techniques and how the optimization principles from this course apply to future developments. By the end, you'll have hands-on experience with the caching strat