SGLang
SGLang is an open-source high-performance serving framework for large language and vision-language models, known for RadixAttention prefix caching and fast structured generation.
SGLang is an open-source serving framework for large language and multimodal models. Its own documentation describes it as a high-performance serving framework for large language and multimodal models, built for production-level serving. It targets one problem that dominates real inference bills: throughput and latency when many requests share overlapping context. SGLang tackles this with RadixAttention, a prefix-cache scheme that reuses the key-value cache across requests that begin with the same tokens. It pairs that with a fast engine for structured and constrained generation, so JSON and schema-bound outputs decode quickly. SGLang is released under the Apache 2.0 license.
Where SGLang sits
SGLang is the runtime layer between your model weights and your application. It loads a model, batches incoming requests, manages the KV cache, and exposes an OpenAI-compatible HTTP API. Your app talks to that API the same way it would talk to a hosted provider.
What RadixAttention does
Most chat and agent workloads repeat context. A system prompt, a few-shot example set, or a shared document prefix appears in request after request. Recomputing that shared prefix for every request wastes GPU time. RadixAttention stores computed KV-cache entries in a radix tree, a structure that indexes strings by shared prefixes. When a new request arrives, SGLang matches the longest cached prefix and reuses that work instead of recomputing it. The framework documentation reports up to 5x faster inference with RadixAttention on prefix-heavy workloads, and 3x faster JSON decoding with its compressed finite state machine for structured output. Treat those figures as vendor-reported and benchmark them against your own traffic.
This matters most for inference patterns with heavy prefix sharing: RAG pipelines with a fixed instruction block, multi-turn chat, and agent loops that resend the same tool descriptions on every step.
How to use it
Install SGLang with pip and launch a server. The server exposes an OpenAI-compatible API, so existing client code works with a changed base URL.
# Install (Python 3.10 or higher required)
pip install sglang
# Launch a server
python3 -m sglang.launch_server \
--model-path meta-llama/Llama-3.1-8B-Instruct \
--host 0.0.0.0 \
--port 30000Call it from any OpenAI-compatible client:
from openai import OpenAI
client = OpenAI(base_url="http://localhost:30000/v1", api_key="none")
resp = client.chat.completions.create(
model="meta-llama/Llama-3.1-8B-Instruct",
messages=[{"role": "user", "content": "Summarise this in one line."}],
)
print(resp.choices[0].message.content)The typical path from model to production looks like this.
SGLang also serves vision-language models such as LLaVA-OneVision, plus embedding and reward models, so a single runtime can cover several model types in one stack.
How it compares
SGLang competes with other open serving runtimes. The right choice depends on your hardware, model, and how much your traffic shares context.
| SGLang | vLLM | TGI | TensorRT-LLM | |
|---|---|---|---|---|
| License | Apache 2.0 | Apache 2.0 | Apache 2.0 | Apache 2.0 |
| Maintainer | SGLang project | vLLM project | Hugging Face | NVIDIA |
| Signature feature | RadixAttention prefix reuse | PagedAttention KV cache | Managed HF serving | NVIDIA GPU optimisation |
| Structured output | Fast constrained decoding | Supported | Supported | Supported |
| Hardware | Single GPU to clusters | Broad GPU support | Broad GPU support | NVIDIA GPUs only |
| Best for | Prefix-heavy, structured workloads | General open serving | Hugging Face stacks | Peak NVIDIA throughput |
For the Hugging Face-native option, see Text Generation Inference . For the NVIDIA-optimised path, see TensorRT-LLM . All four use forms of paged KV-cache management and continuous batching to keep GPUs busy.
When not to use it
- You want a managed endpoint, not a runtime. SGLang serves models you host on GPUs you rent or own. If you prefer a fully hosted API, use a provider such as Groq , Fireworks AI , or Together AI .
- Your traffic has little prefix overlap. RadixAttention’s main advantage is reusing shared context. If every request is unique with no common prompt, that benefit shrinks and a general runtime may serve you as well.
- You are locked to a closed model. SGLang serves open-weight models. It does not run proprietary models you cannot download.
- You have no GPU to run it on. SGLang needs GPU hardware. Compare rental options in GPU clouds and neoclouds .
- You need zero-ops simplicity for a tiny prototype. Running and scaling a serving framework adds operational work. For a first prototype, a hosted API is faster to reach.
Further reading
- SGLang documentation : official install, server launch, and feature guides.
- SGLang on GitHub : source, README, and release notes.
- What is inference? : the runtime step SGLang optimises.
- What is the KV cache? : the memory RadixAttention reuses across requests.
- Continuous batching : how serving frameworks keep GPUs busy.
- Text Generation Inference : the Hugging Face serving alternative.
- GPU clouds and neoclouds : where to rent the hardware SGLang runs on.
Sources
- SGLang project, GitHub repository. https://github.com/sgl-project/sglang
- SGLang documentation. https://docs.sglang.io/
- SGLang installation guide. https://docs.sglang.io/get_started/install.html