SGLang: Fast LLM Serving and a Structured Generation Programming Model

SGLang is two things in one package: a high-throughput serving runtime and a frontend language for writing structured, multi-step LLM programs. The runtime is interesting because of RadixAttention, which automatically reuses the key-value cache across requests that share a prefix, and the frontend lets you express forks, constrained outputs, and control flow without manually stitching prompts together. This tutorial walks through both pillars with a coherent extraction-and-reasoning example you can adapt to real workloads.

What SGLang Is

It helps to separate the two layers from the start, because most confusion about SGLang comes from conflating them.

The runtime is a server you launch once and query over HTTP. It handles batching, scheduling, and KV-cache management on the GPU. You can use it through an OpenAI-compatible API and never touch the frontend language at all.

The frontend language is a Python DSL (embedded as decorators and helper functions) for describing generation programs. It compiles your program into a sequence of calls against a backend, which can be the local SGLang runtime, or an external provider such as OpenAI. The frontend is where features like parallel forks and constrained decoding become ergonomic.

The two pillars

Pillar 1 — the runtime with RadixAttention. Continuous batching keeps the GPU busy by adding and removing requests from the running batch at the token level rather than waiting for a whole batch to finish. RadixAttention adds automatic prefix-cache reuse: the runtime maintains a radix tree of previously seen token sequences and their KV cache, so when a new request shares a prefix (a system prompt, a few-shot block, a shared document) the runtime skips recomputing it. Pillar 2 — the frontend language. Instead of building one large string and parsing the response, you compose a program: append messages, call sgl.gen to fill in slots, branch with s.fork, and constrain outputs with choices, regex, or a JSON schema. The frontend tracks state and can run independent branches in parallel.

How this differs from vLLM

If you have used vLLM, the runtime will feel familiar: both do continuous batching, tensor parallelism, and expose an OpenAI-compatible API. The practical differences are that SGLang's RadixAttention makes shared-prefix workloads (few-shot prompting, agents that reuse a long system prompt, tree-of-thought style branching) noticeably cheaper, and that SGLang ships a frontend language for orchestrating multi-step programs. vLLM focuses on being a serving engine; SGLang is a serving engine plus a programming model. We will not re-explain PagedAttention here.

Installation and Hardware Notes

The simplest install pulls the runtime, the frontend, and the common dependencies.

# Recommended: install everything (runtime + frontend + extras)
pip install "sglang[all]"

Frontend only, if you just want the DSL against a remote backend
pip install "sglang[openai]"

Verify
python -c "import sglang as sgl; print(sgl.version)"

Hardware notes worth keeping in mind:

• The runtime targets NVIDIA GPUs with recent CUDA. Ampere (A100) and newer (L4, L40S, H100) are the smooth path; some kernels assume sm80 or higher.
• Memory governs how much KV cache you can hold and therefore how much RadixAttention can reuse. More free VRAM means a larger radix tree and higher cache-hit rates.
• AMD ROCm support exists but is less battle-tested; check the current support matrix before committing.
• For multi-GPU, tensor parallelism (--tp) shards the model weights across GPUs on one node.

# A quick environment sanity check
python -c "import torch; print('cuda', torch.cuda.isavailable(), 'gpus', torch.cuda.devicecount())"

nvidia-smi --query-gpu=name,memory.total,memory.used --format=csv

Launching the Server

The server is a single command. Start with a small model to confirm the install, then scale up.

python -m sglang.launchserver \
  --model-path meta-llama/Llama-3.1-8B-Instruct \
  --port 30000 \
  --host 0.0.0.0

Common flags you will reach for:

# Multi-GPU with tensor parallelism (4 GPUs on one node)
python -m sglang.launchserver \

  --model-path meta-llama/Llama-3.1-70B-Instruct \
  --tp 4 \
  --port 30000