Ollama vs SGLang
Both are catalogued under Model Serving. The figures come from the GitHub API; the assessments are ours.
OllamaRun open models locally with one commandOfficialMIT
SGLangServing engine tuned for prefix reuse and structured outputOfficialApache-2.0At a glance
| At a glance | Ollama | SGLang |
|---|---|---|
| License | MIT | Apache-2.0 |
| Languages | Go | Python |
| Deployment | Runs locally / Self-hosted | Self-hosted |
| Maturity | Established | Established |
| Stars | 179k | 31.9k |
| Star growth over the last 7 days | +21 ★ | +10 ★ |
| Forks | 17.4k | 7.9k |
| Open issues | 3.7k | 4.9k |
| Last commit | 15 Aug 2026 | 15 Aug 2026 |
| Activity | Active | Active |
What each one does
Ollama
Handles model download, quantisation and an OpenAI-compatible server so a local model is a single command away. The obvious starting point for development against local inference. For serving many concurrent users, a throughput-oriented server will do considerably better.
Full entry →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 →What you can do
Ollama
- Run a model in one command —
ollama run gemma4pulls the weights and opens a chat session, with the full catalogue of runnable models at ollama.com/library. - Back a coding agent locally —
ollama launch claudestarts the Claude Code integration against local models, and the same command covers Codex, Copilot CLI, OpenCode, Droid and DeepSeek Harness. - Call it from your code — A REST endpoint at
http://localhost:11434/api/chataccepts JSON withmodel,messagesandstream, and official clients install withpip install ollamaornpm i ollama. - Turn it into an assistant —
ollama launch openclawconnects the local model to WhatsApp, Telegram, Slack and Discord as a personal AI assistant.
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.