sglang
SGLang is a fast serving framework for large language models and vision language models.
Decision gist · record as of 2026-08-14
Yes, if you are deploying language models or multimodal models in production and need low-latency, high-throughput serving. SGLang is actively maintained, has no known vulnerabilities, and is permissively licensed under Apache 2.0. The 73 dependencies and requirement for Python ≥3.10 plus GPU/TPU hardware are expected trade-offs for a specialized inference framework. Install it if your workload involves serving models at scale; skip it if you only need local, single-model inference without distributed optimization.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python ≥3.10 and a compatible GPU (NVIDIA, AMD, Intel, Google TPU, or Ascend NPU) or CPU; CUDA/ROCm libraries must be installed separately for GPU acceleration.
- Medium install friction due to 73 runtime dependencies including compiled libraries (cuda-python, apache-tvm-ffi, ninja, numba) and specialized packages (flash-attn-4, flashinfer_python).
- Prebuilt wheels available for Python 3.10–3.13 on x86_64 and aarch64 Linux.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions—you must include a copy of the license and document changes, but no reciprocal licensing is required.
last release 2026-08-08 (6 days) · last repo commit 2026-08-14 · 31,759 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 102,289,382 downloads/mo, #336 on PyPI
Alternatives
Verify before relying
pip install sglang
import sglang as sgl
from sglang import function, gen
@function
def chat(s, question):
s += question
s += gen("answer")
state = chat.run(question="What is AI?")- Exact performance gains (e.g., '5x faster inference', '3x faster JSON decoding') claimed in description are not quantified in the fact sheet.
- Specific model support list (Llama, Qwen, DeepSeek, etc.) is mentioned but not enumerated in the fact sheet.
- Compatibility with 'most Hugging Face models' is stated but not formally tested or certified in the fact sheet.
- Claim of 'powering over 400,000 GPUs worldwide' lacks verification in the fact sheet.
What it is and what it does
SGLang is a production-grade serving framework designed to run large language models and multimodal models efficiently on GPUs and TPUs. It abstracts away the complexity of distributed inference, offering features like prefix caching (RadixAttention), continuous batching, speculative decoding, and support for multiple parallelism strategies (tensor, pipeline, expert, data). The framework handles both language models and diffusion models, with built-in support for structured outputs, quantization (FP4/FP8/INT4/AWQ/GPTQ), and multi-LoRA batching.
You use SGLang when you need to serve models at scale—whether on a single GPU or across a cluster—and want to minimize latency while maximizing throughput. It integrates with fastapi for HTTP serving, supports OpenAI-compatible APIs, and is actively used as a rollout backend for reinforcement learning and post-training workflows. The 73 runtime dependencies (including cuda-python, apache-tvm-ffi, flash-attn-4, and specialized kernels) reflect its tight integration with GPU compute stacks; installation requires Python ≥3.10 and a supported accelerator.
Use it for
- Deploy a large language model as a low-latency HTTP service using fastapi and SGLang's runtime scheduler.
- Run multimodal inference with continuous batching and paged attention on supported GPU hardware.
- Scale inference across multiple GPUs using tensor or expert parallelism for large models.
- Serve structured JSON outputs from a language model with compressed finite-state machine decoding.
- Use SGLang as a rollout backend for reinforcement learning training pipelines with native RL integrations.
- Accelerate video and image generation with SGLang Diffusion on supported hardware.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are deploying language models or multimodal models in production and need low-latency, high-throughput serving.
SGLang is actively maintained, has no known vulnerabilities, and is permissively licensed under Apache 2.0. The 73 dependencies and requirement for Python ≥3.10 plus GPU/TPU hardware are expected trade-offs for a specialized inference framework. Install it if your workload involves serving models at scale; skip it if you only need local, single-model inference without distributed optimization.
Install
sglang on PyPI
Before you install
Medium install friction due to 73 runtime dependencies including compiled libraries (cuda-python, apache-tvm-ffi, ninja, numba) and specialized packages (flash-attn-4, flashinfer_python). Prebuilt wheels available for Python 3.10–3.13 on x86_64 and aarch64 Linux. Active maintenance with a recent release and strong community adoption.
Requires Python ≥3.10 and a compatible GPU (NVIDIA, AMD, Intel, Google TPU, or Ascend NPU) or CPU; CUDA/ROCm libraries must be installed separately for GPU acceleration.
License in practice
Apache License 2.0 (permissive) allows commercial use, modification, and distribution with minimal restrictions—you must include a copy of the license and document changes, but no reciprocal licensing is required.
Quickstart
pip install sglang
import sglang as sgl
from sglang import function, gen
@function
def chat(s, question):
s += question
s += gen("answer")
state = chat.run(question="What is AI?")Verify before relying
- Exact performance gains (e.g., '5x faster inference', '3x faster JSON decoding') claimed in description are not quantified in the fact sheet.
- Specific model support list (Llama, Qwen, DeepSeek, etc.) is mentioned but not enumerated in the fact sheet.
- Compatibility with 'most Hugging Face models' is stated but not formally tested or certified in the fact sheet.
- Claim of 'powering over 400,000 GPUs worldwide' lacks verification in the fact sheet.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 73 packagesaiohttpanthropicapache-tvm-ffiavblobfilebuildcompressed-tensorscuda-pythondatasetsdecord2distroeasydicteinopsfastapiflash-attn-4flashinfer_pythonggufhelionhumming-kernelsinteregularIPythonkernelsllguidancemistral_commonmodelscopemsgspecninjanumbanumpynvidia-cutlass-dsl |
| Maintenance | Actively maintained 6 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 102,289,382 / month, #336 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3 |
Evidence: sglang-0.5.17-cp310-cp310-manylinux_2_34_aarch64.whl; sglang-0.5.17-cp310-cp310-manylinux_2_34_x86_64.whl; sglang-0.5.17-cp311-cp311-manylinux_2_34_aarch64.whl; sglang-0.5.17-cp311-cp311-manylinux_2_34_x86_64.whl; sglang-0.5.17-cp312-cp312-manylinux_2_34_aarch64.whl; sglang-0.5.17-cp312-cp312-manylinux_2_34_x86_64.whl; sglang-0.5.17-cp313-cp313-manylinux_2_34_aarch64.whl; sglang-0.5.17-cp313-cp313-manylinux_2_34_x86_64.whl
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See also vllm · sgl-kernel · vllm-tpu · sglang-kernel · sglang-router · deepspeed · smg-grpc-servicer · verl · lmcache · ipex-llm