SGLang

SGLang is a freely available piece of software that runs already-trained language models. A language model is a program that computes a response word by word from an input – for instance, a chatbot. Such a model is initially just a huge file of numbers. To turn it into a usable service, you need a program that accepts the requests, organizes the computation on the graphics cards, and sends the text back. This is exactly the role SGLang plays. The name stands for “Structured Generation Language,” because the project also brings along tools to prescribe the form of the response.

Why this kind of server software determines the costs

Training a large model is expensive, but it happens only once. Operating it, on the other hand, runs continuously. Every single response to every single user costs computing time on graphics cards, and those are expensive and scarce. Anyone offering a service with millions of requests per day feels even small efficiency gains immediately in the bill.

That’s why the choice of operating software is not a side issue. Two servers can load the same model and still serve very different numbers of users per graphics card. The difference lies in how cleverly the software bundles requests and how little it computes redundantly. SGLang is known for achieving particularly good results here.

A second reason is independence. Anyone who runs a model themselves does not have to send their data to an external provider. For banks, hospitals, or government agencies, this is often decisive. Free software like SGLang makes this path practically possible without needing an in-house team of specialists in graphics-card programming.

Reusing instead of recomputing

A language model computes intermediate results for every word it reads and stores them in a cache. On the next request, it normally starts over from scratch. Very often, though, requests share an identical beginning: the same instruction, the same document, the same conversation history. SGLang’s key idea is called RadixAttention: it arranges these intermediate results in a tree structure in which shared beginnings exist only once.

You can picture this like a family tree. Requests with the same beginning share the trunk and only branch off where their text diverges. The shared part only needs to be computed once. In applications with many similar requests, this brings a multiple increase in throughput.

The second building block is enforced response formatting. Often a model should not deliver free text but rather a data structure – for example, a list of name, date, and amount. While generating text, SGLang can block all words that would violate this form. The result is machine-readable without needing any cleanup afterward. The project should be distinguished from pure training libraries: SGLang trains nothing, it only runs already-finished models.

Who uses SGLang

SGLang is rarely visible directly. You encounter it as an invisible layer behind chat services, coding assistants, and search functions of companies that host their own models. Providers who offer paid access to open models also frequently use this kind of server software.

In tech news, the name usually comes up in comparison with vLLM, the best-known competing project. Both pursue the same goal, both are open, and both regularly publish benchmark figures on how many requests per second they can handle. When a lab releases a new open model, the announcement often states which of these servers support it from day one.

A common misconception is that SGLang makes a model smarter. It does not. The quality of the responses comes from the model itself. SGLang only changes how fast, how cheaply, and in what form these responses reach the user.