What Is SGLang? Architecture, Performance, and When to Use It Over vLLM

Most teams serving LLMs in production start with vLLM. It is the default, the docs are everywhere, and it works.

Then the workload changes. You add an agent loop that sends the same 2,000-token system prompt on every call. Or a RAG pipeline where half the context repeats across requests. Or a chat product where every turn re-sends the conversation history. Suddenly your GPUs are spending most of their time recomputing tokens they have already processed.

This is the exact problem SGLang was built to solve.

What SGLang Actually Is

SGLang is an open-source serving framework for large language models and multimodal models, hosted under LMSYS, the same non-profit behind Chatbot Arena. It came out of research at Stanford and UC Berkeley and has grown into one of the most widely deployed inference engines in production.

SGLang at a glance

The adoption numbers are not small. According to the SGLang project, it generates trillions of tokens per day across more than 400,000 GPUs, with users including xAI, LinkedIn, Cursor, AMD, and NVIDIA. The project is Apache 2.0 licensed and ships frequent releases (v0.5.11 landed May 5, 2026).

Like vLLM, it exposes an OpenAI-compatible API, supports continuous batching and paged attention, and runs most open models: Llama, Qwen, DeepSeek, Kimi, GLM, Mistral, and the rest of the usual list.

So why does it exist when vLLM already does all that?

The Key Innovation: RadixAttention

The short answer is prefix reuse.

When an LLM processes a request, it builds a KV cache for every token in the prompt. Most serving systems throw that cache away when the request finishes. If the next request starts with the same tokens, the GPU computes the whole thing again.

RadixAttention keeps the KV cache in a radix tree, a data structure that makes shared prefixes easy to find. When a new request arrives, SGLang walks the tree, finds the longest prefix it has already computed, and only processes the new tokens.

No manual configuration. No cache keys to manage. The engine discovers shared prefixes on its own.

Where this pays off:

• Multi-turn chat. Every turn re-sends the conversation history. With RadixAttention, turn 20 only computes the newest message instead of the full transcript.
• Agent workloads. Agents hammer the same system prompt and tool definitions hundreds of times per session. That entire prefix gets computed once.
• RAG pipelines. Shared instruction templates and repeated document chunks hit the cache instead of the GPU.

The original LMSYS results claimed up to 5x faster inference on these workload types (January 2024, vendor-published, so validate on your own traffic before planning capacity around it). The pattern third-party benchmarks keep finding in 2026 is consistent: on unique-prompt batch jobs, SGLang and vLLM are close to even. On prefix-heavy workloads, the gap gets wide.

The Second Trick: Fast Structured Output

SGLang began life as a language for structured generation (the name stands for Structured Generation Language), and it still has an edge there.

When you constrain output to a JSON schema, the engine has to check every generated token against a grammar. SGLang compiles that grammar into a compressed finite state machine that can skip ahead through portions of the output where only one token is valid. Instead of generating {"name": " token by token, it emits the whole constrained span in one step.

LMSYS reported up to 3x faster JSON decoding from this technique. If your product extracts structured data, calls tools, or returns typed responses, this is not a niche feature. It is most of your traffic.

Where vLLM Still Wins

An honest comparison cuts both ways.

vLLM has the larger ecosystem: more integrations, more deployment guides, more engineers who already know it, and the fastest path from zero to serving. Its PagedAttention memory management remains excellent for high-concurrency workloads with diverse prompts, and for one-shot batch inference where no prefixes repeat, there is no caching advantage for SGLang to exploit.

If your workload is mostly unique prompts at high volume, vLLM is still a strong default. We ran the numbers side by side in our vLLM vs SGLang comparison, and for the full four-way picture there is our inference engine roundup covering TensorRT-LLM and TGI as well.

The decision usually reduces to one question: how much of your traffic shares a prefix? Above roughly half, SGLang's architecture starts working in your favor. Below that, the engines are closer than the benchmarks suggest.

Hardware Support Is Broader Than You Might Expect

SGLang is not an NVIDIA-only story. The project runs on NVIDIA GPUs from the A100 through GB200 and B300, AMD Instinct MI300 and MI355, Intel Xeon CPUs, Google TPUs (natively, via the SGLang-Jax backend released October 2025), and Ascend NPUs.

That breadth matters if you are trying to avoid betting your serving stack on one silicon vendor. It is also why SGLang keeps showing up in multi-silicon deployments: the engine moves with you when the hardware mix changes. There is even a lightweight community port targeting AWS silicon, which we covered in our post on Mini-SGLang on Trainium and Inferentia.

One more recent development worth knowing: SGLang Diffusion (January 2026) extends the same serving infrastructure to video and image generation models, and the RL community has adopted SGLang as a rollout backend in frameworks like verl and AReaL.

Running SGLang in Production

The engine is the easy part. A single docker run gets you a serving endpoint.

Production is where the real questions start: which GPU, how many replicas, what happens when traffic spikes, and what you pay when it does not. RadixAttention also rewards cache-aware routing. If your load balancer scatters a user's session across replicas, prefix hits drop and the throughput advantage shrinks.

On Yotta Labs, the usual pattern is running SGLang in a container on GPU Pods for steady traffic, or on Serverless when load is bursty and you want scale-to-zero. Custom images let you pin your exact SGLang version and flags instead of taking whatever an endpoint provider gives you.

FAQ

Is SGLang free?
Yes. Apache 2.0, open source, hosted by the non-profit LMSYS organization.

Is SGLang faster than vLLM?
On workloads with heavy prefix reuse (chat, agents, RAG), usually yes, and sometimes by a lot. On unique-prompt batch workloads, they are roughly even. Benchmark your own traffic before deciding.

What does SGLang stand for?
Structured Generation Language. It started as a programming language for structured LLM output and grew into a full serving framework.

What models does SGLang support?
Most major open models: Llama, Qwen, DeepSeek, Kimi, GLM, Gemma, Mistral, plus embedding, reward, and diffusion models. It is compatible with most Hugging Face models and exposes an OpenAI-compatible API.

Does SGLang run on AMD GPUs?
Yes. MI300 and MI355 are supported, along with Google TPUs, Intel CPUs, and Ascend NPUs.

Do I need SGLang if I already use vLLM?
Not necessarily. If your prompts rarely share prefixes and your structured-output needs are light, vLLM is fine. Switch when cache reuse or constrained decoding becomes a measurable cost.

Bottom Line

SGLang earned its place in the serving stack by being fast where modern workloads actually live: repeated prefixes, multi-turn sessions, agents, and structured output. vLLM is still the broader default, but if your traffic looks like 2026 traffic rather than 2023 traffic, SGLang deserves a benchmark run.

When you are ready to test it on real hardware, you can launch a GPU Pod with your own SGLang image in a few minutes, or read our vLLM vs SGLang breakdown first to see which engine fits your workload.