SGLang

What is SGLang?

SGLang is an open-source inference framework for large language, vision-language and diffusion models, built for agentic workloads and large-scale serving. You launch a model with `sglang serve` and call it over an OpenAI-style /v1/chat/completions endpoint. It runs on NVIDIA GPUs and also supports AMD Instinct, Google TPU, Intel GPUs and Xeon CPUs, Apple Silicon and Huawei Ascend, and the project says it serves production workloads across AI labs, cloud platforms and enterprises.

Where it fits in a 10–200 person business

• Serve open-weight models to internal apps through one OpenAI-style endpoint
• Share one GPU server between many users, agents or batch jobs instead of desktop runners per person
• Move from a single-user runner such as Ollama to a production serving stack on your own GPUs

These use cases are our editorial suggestions, not claims made by the project.

Can a business use SGLang? (license)

This is a plain-English summary of the license text, not legal advice. Third-party models you run with it have their own licenses.

Data & compliance: does SGLang phone home?

Checked against the project's own docs and repository on Oct 31, 2026. "Not documented" means we could not find the answer in official sources; it does not mean the feature is missing or that no data is sent. Third-party services you connect (model APIs, vector databases, search engines) have their own data policies. Compare all apps.

What hardware does SGLang need?

The project does not publish a minimum CPU/RAM figure, so we leave the official value blank rather than guess.

GPU: Yes for the documented install path: Linux with a supported NVIDIA GPU and a CUDA 13-compatible driver. The project also documents AMD Instinct, Google TPU, Intel GPU and Xeon CPU, Ascend NPU and other platforms on separate hardware pages.

No official CPU, RAM or VRAM minimum is published; VRAM depends on the model and context length. The Docker example uses a 32 GB shared-memory size.

Source: github.com/sgl-project/sglang#supported-hardware

How much does it cost to host SGLang per month?

How to deploy SGLang

Method: Docker (NVIDIA GPU)

HF_CACHE_DIR="/path/to/huggingface-cache"
docker run -it --gpus all \
  --shm-size 32g \
  -p 30000:30000 \
  -v "$HF_CACHE_DIR:/root/.cache/huggingface" \
  --ipc=host \
  lmsysorg/sglang:latest bash
# inside the container:
sglang serve MODEL_PATH --host 0.0.0.0 --port 30000

From the official instructions: docs.sglang.io/docs/get-started/install. Always check the source for the current version before you run it.

Known gotchas when self-hosting SGLang

• SGLang now requires CUDA 13. CUDA 12 images are no longer published, and lmsysorg/sglang:v0.5.19-cu129 is the last CUDA 12 tag, so check your host driver first. source
• The default FlashInfer attention backend only supports sm75 and newer GPUs. If you hit FlashInfer errors, add --attention-backend triton --sampling-backend pytorch. source
• When installing with uv older than 0.12.0, leaving out --prerelease=allow silently installs the old SGLang 0.5.9. source
• The official Docker command needs the NVIDIA Container Toolkit configured, plus --ipc=host and a large --shm-size (32g in the example); mount your Hugging Face cache so weights aren't downloaded again. source

Alternatives in Local model runtimes

• Ollama The simplest way to download and run open-weight LLMs locally behind an API. Compare Ollama vs SGLang
• llama.cpp Lightweight C/C++ engine that runs quantized open models on CPU or GPU, with an OpenAI-compatible server. Compare llama.cpp vs SGLang
• vLLM High-throughput LLM inference server for GPUs with an OpenAI-compatible API. Compare SGLang vs vLLM
• LocalAI OpenAI-compatible API for LLMs, vision, voice and images on any hardware. Compare LocalAI vs SGLang
• TextGen (text-generation-webui) Local LLM app (formerly text-generation-webui) with a browser UI, OpenAI/Anthropic-compatible API and LoRA training. Compare SGLang vs TextGen (text-generation-webui)
• Xinference Model-serving platform for LLM, embedding, rerank, speech and image models behind one OpenAI-compatible API. Compare SGLang vs Xinference
• GPUStack GPU cluster manager that deploys vLLM, SGLang and other engines behind an OpenAI-compatible API. Compare GPUStack vs SGLang

Sources

• GitHub repository: github.com/sgl-project/sglang
• Official documentation, Deploy instructions, Gotcha 1, Gotcha 2, Gotcha 3, Gotcha 4: docs.sglang.io/docs/get-started/install
• License text: github.com/sgl-project/sglang/blob/main/LICENSE
• Hardware requirements: github.com/sgl-project/sglang#supported-hardware
• Hetzner Cloud pricing (cost estimate): www.hetzner.com/cloud/regular-performance
• GitHub stars, commits and releases: GitHub REST API, fetched Oct 31, 2026