AI Frameworks · sgl-project

sglang

SGLang is an open-source, high-performance serving framework for large language models and multimodal models. It delivers low-latency, high-throughput inference across single GPUs and distributed clusters, with broad hardware support (NVIDIA, AMD, Intel, TPU) and extensive model compatibility.

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Source: GitHub — github.com/sgl-project/sglang

30.1k

GitHub stars

Forks

Python

Primary language

Apache-2.0

License (OSI-approved)

Key facts

Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.

Field	Value
Repository	sgl-project/sglang
Owner	sgl-project
Primary language	Python
License	Apache-2.0 — OSI-approved
Stars	30.1k
Forks	7k
Open issues	4k
Latest release	v0.5.14 (2026-06-26)
Last updated	2026-07-08
Source	https://github.com/sgl-project/sglang

What sglang is

SGLang provides optimized LLM inference via RadixAttention prefix caching, zero-overhead scheduling, prefill-decode disaggregation, speculative decoding, paged attention, tensor/pipeline/expert parallelism, structured outputs, and quantization (FP4/FP8/INT4/AWQ/GPTQ). Supports 400+ Hugging Face models, embedding models, reward models, and diffusion models with native RL integrations for post-training workloads.

Quickstart

Get the sglang source

Clone the repository and explore it locally.

terminalbash

1git clone https://github.com/sgl-project/sglang.git2cd sglang3# follow the project's README for install & configuration

Need it deployed, integrated, or customized instead? DEV.co ships production installs.

Best use cases

High-Throughput LLM API Services

Deploy scalable inference backends for chatbots, RAG systems, and content generation at production scale. SGLang's continuous batching, speculative decoding, and distributed parallelism enable efficient serving across large request volumes with low per-token latency.

Multi-Modal Inference (Vision + Text)

Serve multimodal models (LLaVA, Qwen-Image, video understanding) with optimized scheduling. RadixAttention caching and chunked prefill reduce redundant computation across multi-image and multi-video inputs, improving throughput for vision-language tasks.

RL & Post-Training Workflows

Use SGLang as a proven rollout backend for reinforcement learning pipelines. Native integrations with AReaL, Miles, veRL, and Tunix; supports fast batch generation and reward model scoring required for frontier model training.

Implementation considerations

Requires NVIDIA CUDA, AMD ROCm, or TPU (JAX) toolchain. Ensure target hardware drivers and libraries are available and compatible with the SGLang release version.
Model registration and custom kernel optimization may be necessary for cutting-edge or proprietary architectures. Review integration docs and community examples before committing to unsupported model families.
Distributed setup (multi-GPU/multi-node) demands careful configuration of tensor/pipeline/expert parallelism strategies; benchmark and tune based on your hardware topology and batch patterns.
Quantization (FP4/FP8/INT4/AWQ/GPTQ) support varies by model. Validate quantization compatibility and verify accuracy/throughput trade-offs for your workload before production deployment.
LoRA multi-batching and speculative decoding have operational overhead; profile your request mix (latency vs. throughput sensitivity) to determine optimal scheduler and decoding strategy.

When to avoid it — and what to weigh

GPU-Scarce or Edge-Only Deployments — SGLang is optimized for GPU clusters and datacenter workloads. While CPU inference is listed, performance and active development focus GPU acceleration; CPU-only or embedded edge scenarios may lack mature support.
Minimal Dependencies & Small Footprint — SGLang is a comprehensive framework with non-trivial dependencies (PyTorch, CUDA/ROCm, system libraries). If you need a lightweight, pure-Python inference solution, lighter alternatives may be more appropriate.
Static, One-Off Model Deployment — SGLang excels at continuous serving and batch inference. If you only need to run inference once or infrequently on a single small model, the setup overhead may not justify the architecture.
Proprietary/Closed-Source Deployment Requirements — SGLang is Apache-2.0 licensed (open-source). Verify your commercial terms allow Apache-2.0 dependencies; some enterprises restrict open-source binaries without legal review.

License & commercial use

SGLang is licensed under Apache License 2.0 (Apache-2.0), a permissive, OSI-approved open-source license. Apache-2.0 permits commercial use, modification, distribution, and private deployment, with the conditions that you retain license notices and provide a CHANGES file documenting modifications. No royalty or proprietary source-code disclosure is required.

Apache-2.0 is commercially permissive. You may use, modify, and deploy SGLang in commercial products without royalty or vendor approval. Ensure your legal team reviews any dependencies SGLang pulls in (PyTorch, CUDA, etc.) for compliance with your commercial licensing policy. SGLang itself does not impose additional commercial restrictions, but verify NVIDIA/AMD SDK terms if proprietary backend changes are required.

DEV.co evaluation signals

Editorial assessment — not user reviews. Directional, with an explicit confidence level.

Signal	Assessment
Maintenance	Active
Documentation	Strong
License clarity	Clear
Deployment complexity	Moderate
DEV.co fit	Strong
Assessment confidence	High

Security considerations

No formal security audit data provided. SGLang is open-source, allowing community review, but maturity and production hardening are dependent on your use case. Considerations: (1) Validate input/prompt sanitization for untrusted user queries to prevent prompt injection or token leakage. (2) Review CUDA/ROCm kernel security; ensure GPU firmware is patched. (3) Multi-tenant scenarios require isolation guarantees not explicitly stated in available docs—verify sandbox/isolation strategy. (4) Dependency supply-chain risk: SGLang pulls in PyTorch, CUDA, and other frameworks; scan for CVEs and manage updates. No evidence of formal vulnerability disclosure process; verify community disclosure norms.

Alternatives to consider

vLLM

Mature, widely adopted OSS LLM serving framework with PagedAttention. Simpler to deploy for single-model workloads; less emphasis on distributed/RL use cases. Larger user base but possibly less cutting-edge optimization for latest models.

TensorRT-LLM (NVIDIA)

Proprietary NVIDIA optimization framework for CUDA-based inference. Highest per-token performance on NVIDIA hardware but requires manual model compilation and optimization; less flexible for rapid model iteration or multi-vendor hardware.

Ollama

Lightweight, user-friendly local LLM runner with built-in model library. Ideal for single-user or edge scenarios; much lower deployment complexity but sacrifices distributed performance and RL training integration.

Software development agency

Build on sglang with DEV.co software developers

SGLang powers 400k+ GPUs and offers production-grade serving, RL integration, and broad hardware support. Assess if its distributed complexity and GPU-first design fits your scale and timeline. Contact our team for architecture review and deployment planning.

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sglang FAQ

Can I run SGLang on CPU only?

SGLang lists Intel Xeon CPU support, but GPU is the primary optimization target. CPU-only inference will be significantly slower. Verify CPU-specific performance and feature completeness for your model before committing.

Does SGLang support fine-tuning or training?

SGLang is primarily a serving/inference framework. It is used as a rollout backend for RL and post-training workflows (e.g., via Miles, veRL) but does not natively include fine-tuning tools. Model training should occur upstream; SGLang serves inference.

How do I deploy SGLang in production with auto-scaling?

SGLang works with Kubernetes and Ray for orchestration. Multi-GPU distribution is supported via parallelism strategies (tensor, pipeline, expert). Details on auto-scaling triggers and Kubernetes CRDs require review of official docs and community examples.

Is quantization (INT4/FP8) mandatory for large models?

Quantization is optional but recommended to reduce memory footprint and latency. Support varies by model; benchmark quantized vs. full-precision accuracy and throughput for your workload. SGLang supports AWQ, GPTQ, FP4, FP8, and INT4 formats.

Work with a software development agency

Adopting sglang is usually one piece of a larger software development effort. As a software development agency, DEV.co provides software development services and web development expertise — pairing senior software developers and web developers with your team to design, build, and operate ai frameworks software in production.

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