Efficient Inference with SGLang: Text and Image Generation
by DeepLearning.AI, LMSys, RadixArk
Build a KV cache from scratch, then use SGLang's RadixAttention to share it across requests and cut LLM and diffusion inference cost.
Overview
Efficient Inference with SGLang: Text and Image Generation is a DeepLearning.AI short course announced on April 9, 2026, built in partnership with LMSys, the non-profit that maintains the open-source SGLang serving framework, and RadixArk. It is taught by Richard Chen, a Member of Technical Staff at RadixArk. The course runs about 1 hour 19 minutes across seven video lessons (Introduction; Overview of Inference; LLM Inference Fundamentals; Advanced LLM Inference Optimization; SGLang Diffusion; The future of inference; Conclusion), three of which include runnable code examples, followed by a graded quiz. Its core argument is that much of the cost of serving LLMs in production comes from redundant computation, because every new request reprocesses the same system prompt and shared context. Learners first build a mental model of token-by-token generation and the memory bottleneck, then implement a KV cache from scratch to store and reuse intermediate attention values within a single request. They then move to RadixAttention, SGLang's technique for sharing KV cache across users and requests by identifying common prefixes in a radix tree, and measure the performance gain. The final technical lesson applies the same caching and parallelism ideas to diffusion models for faster image generation. SGLang itself has 35.9k GitHub stars under Apache-2.0 and supports tensor, pipeline, expert and data parallelism.
At a Glance
- Topic
- Models
- Level
- Intermediate
- Format
- Course
- Cost
- Freemium
- Duration
- ~1h20m of video, 3 code examples plus a quiz, self-paced
- Provider
- DeepLearning.AI, LMSys, RadixArk
- Hands-on
- Yes — code/exercises
- Certificate
- Available
What You’ll Learn
- ✓Trace how an LLM generates output token by token from input to completion
- ✓Explain the memory bottleneck that makes production LLM inference expensive at scale
- ✓Implement a KV cache from scratch to reuse attention values within a request
- ✓Use SGLang RadixAttention to share KV cache across users through radix-tree prefix matching
- ✓Measure the actual performance improvement that cross-request prefix caching delivers in practice
- ✓Apply SGLang caching and parallelism strategies to diffusion models for faster image generation
Highlights
- •Built with LMSys, the non-profit that maintains SGLang (35.9k GitHub stars, Apache-2.0), and taught by a RadixArk engineer
- •You implement the KV cache yourself before touching the framework, so RadixAttention is understood rather than just switched on
- •Rare coverage of diffusion-model serving: the same caching and parallelism ideas applied to image generation, matching SGLang Diffusion released in November 2025
- •Compact at about 1h19m with three code examples, a good conceptual companion to the SGLang documentation rather than a replacement for it
- •Enrollment is free on the DeepLearning.AI platform; the graded quiz and certificate require a Pro membership
Who It’s For
Best For
- ✓ML engineers serving LLMs in production who need to lower GPU cost per request
- ✓Platform teams running agents or chat apps with long shared system prompts where prefix caching pays off
- ✓Engineers serving diffusion image models who want faster generation
Prerequisites
- •Comfortable reading and running Python code
- •Basic knowledge of how language models and attention work
FAQ
What is Efficient Inference with SGLang: Text and Image Generation?
Efficient Inference with SGLang: Text and Image Generation is a 1h19m intermediate DeepLearning.AI short course, built with LMSys and RadixArk, for developers who run LLMs in production. You implement a KV cache from scratch, then apply SGLang's RadixAttention prefix caching and parallelism strategies to both text generation and diffusion image generation.
Is Efficient Inference with SGLang: Text and Image Generation free?
Efficient Inference with SGLang: Text and Image Generation offers free content, with paid options for certificates or premium features.
What level is Efficient Inference with SGLang: Text and Image Generation for?
Efficient Inference with SGLang: Text and Image Generation is aimed at a intermediate audience. Recommended background: Comfortable reading and running Python code, Basic knowledge of how language models and attention work.
How long does Efficient Inference with SGLang: Text and Image Generation take?
Expect roughly ~1h20m of video, 3 code examples plus a quiz, self-paced. Most learners work through it at their own pace.
What will I learn from Efficient Inference with SGLang: Text and Image Generation?
You'll learn: Trace how an LLM generates output token by token from input to completion; Explain the memory bottleneck that makes production LLM inference expensive at scale; Implement a KV cache from scratch to reuse attention values within a request; Use SGLang RadixAttention to share KV cache across users through radix-tree prefix matching; Measure the actual performance improvement that cross-request prefix caching delivers in practice; Apply SGLang caching and parallelism strategies to diffusion models for faster image generation.
Topics
Sources
This page was written from 3 sources, 2 on domains other than deeplearning.ai.