SGLang vs vLLM
Both are catalogued under Model Serving. The figures come from the GitHub API; the assessments are ours.
SGLangServing engine tuned for prefix reuse and structured outputOfficialApache-2.0
vLLMHigh-throughput inference server with an OpenAI-compatible APIOfficialApache-2.0At a glance
| At a glance | SGLang | vLLM |
|---|---|---|
| License | Apache-2.0 | Apache-2.0 |
| Languages | Python | Python, CUDA |
| Deployment | Self-hosted | Self-hosted / Runs locally |
| Maturity | Established | Established |
| Stars | 31.9k | 89.1k |
| Star growth over the last 7 days | +10 ★ | +23 ★ |
| Forks | 7.9k | 20.7k |
| Open issues | 4.9k | 6.7k |
| Last commit | 15 Aug 2026 | 15 Aug 2026 |
| Activity | Active | Active |
What each one does
SGLang
Its prefix cache pays off precisely in the agent case, where many calls share a long, identical system prompt. Also strong at constrained decoding when responses must match a schema. The main alternative in this space is vLLM; benchmark both on your own traffic shape.
Full entry →vLLM
Paged attention and continuous batching let one GPU serve far more concurrent requests than a naive loop, which matters because agents are unusually chatty. The OpenAI-compatible endpoint means most agent frameworks point at it with a base-URL change. Expect real operational work around GPU memory sizing and model loading times.
Full entry →What you can do
SGLang
- Reuse a shared system prompt — RadixAttention prefix caching retains the KV state for identical leading tokens, so repeated agent calls that all carry one long system prompt skip recomputing it.
- Constrain output to a schema — Constrained decoding forces generated responses to match a supplied schema instead of relying on retries to repair malformed tool-call JSON.
- Run it beyond NVIDIA GPUs — The PyPI package is
sglang, and besides NVIDIA GB200/B300/H100/A100 the runtime targets AMD MI355/MI300, Intel Xeon CPUs, Google TPUs and Ascend NPUs. - Reach it with an OpenAI client — The server is compatible with OpenAI APIs and with most Hugging Face models, so code already written against the OpenAI SDK reaches it without a rewrite.
- Reuse the engine for RL rollouts — SGLang has native RL integrations and is used as the rollout backend by post-training frameworks including AReaL, Miles, slime, Tunix and verl.
vLLM
- Serve more concurrent agents per GPU — Paged attention and continuous batching keep many requests in flight on a single card instead of running them through a naive one-at-a-time loop.
- Point existing clients at it — Most agent frameworks switch over to the OpenAI-compatible API server with a base-URL change and no client rewrite, and the same server also speaks the Anthropic Messages API and gRPC.
- Run it on the hardware you have —
uv pip install vllminstalls the server, which runs on NVIDIA, AMD and Intel GPUs and on x86/ARM/PowerPC CPUs, with hardware plugins covering Google TPUs, Intel Gaudi, Huawei Ascend and Apple Silicon. - Shrink a model to fit the card — Quantized weights are served directly — FP8, MXFP8/MXFP4, NVFP4, INT8, INT4, GPTQ/AWQ, GGUF and compressed-tensors are all on the supported list — so the memory a model needs is something you choose rather than a fixed property of the checkpoint.
- Constrain output and parse tool calls — Structured output generation runs through
xgrammarorguidance, and the server ships tool-calling and reasoning parsers for the models that emit them.