Efficient Inference with SGLang: Text and Image Generation

The Batch / 4/9/2026

💬 OpinionDeveloper Stack & InfrastructureTools & Practical Usage

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Key Points

The article presents a short course focused on efficient inference using SGLang for both text and image generation tasks.
It targets intermediate-level practitioners looking to improve performance and throughput when running generative models.
The content emphasizes practical setup and usage patterns for deploying SGLang-based inference workflows.
By covering text and image generation, the course positions SGLang as a unified approach to efficient multimodal inference.

Short CourseIntermediate1 Hour 19 Minutes

Efficient Inference with SGLang: Text and Image Generation

Instructor: Richard Chen

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Intermediate
1 Hour 19 Minutes
7 Video Lessons
3 Code Examples
Instructor: Richard Chen
RadixArk, LMSys

What you'll learn

Understand how LLM inference works token by token, why it gets expensive at scale, and how the KV cache eliminates redundant computation by storing and reusing intermediate values.
Implement SGLang’s RadixAttention to extend caching across users and requests, and measure the real speedups it delivers.
Apply SGLang’s caching and parallelism strategies to diffusion models, accelerating image generation using the same principles as text.

About this course

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 strategies powering today’s most efficient AI systems and the tools to implement these optimizations in your own models at scale.

Who should join?

Developers and ML practitioners who want to better understand and optimize LLM inference in production. Familiarity with Python and basic language model concepts is recommended.