Production-oriented model serving
Runs inference workloads from a single GPU to distributed clusters, with the site positioning the framework for low-latency and high-throughput deployments.
SGLang
Freemium
SGLang is an open-source serving framework for large language and multimodal models. It helps developers deploy model servers and query them through OpenAI-compatible endpoints across supported GPU, CPU, TPU, NPU, and XPU platforms.
What is SGLang?
SGLang is an open-source inference and serving framework for large language and multimodal models. Its core purpose is to run model servers for production workloads, with support for deployments ranging from a single GPU to distributed clusters.
The framework combines model and hardware flexibility with serving optimizations intended to improve latency and throughput. It supports language, vision-language, diffusion, and other multimodal model workloads on platforms including NVIDIA and AMD GPUs, CPU servers, TPUs, Ascend NPUs, and XPUs.
A typical workflow is to install SGLang through pip or Docker, start a server for a selected model, and query it through OpenAI-compatible endpoints.
What can SGLang do?
Broad model coverage
Supports open model families including DeepSeek, Qwen, Llama, Mistral, and GLM, as well as vision-language, diffusion, OCR, speech, and other multimodal models listed in the model catalog.
Cross-platform hardware support
The supported hardware list includes NVIDIA GPUs, AMD GPUs, CPU servers, Google TPUs, Ascend NPUs, and XPUs.
Serving optimizations
Includes disaggregated prefill and decode, speculative decoding, multiple parallelism strategies, a zero-overhead scheduler, and optimized GPU kernels.
OpenAI-compatible API access
After a server is launched, applications can interact with the selected model through standard OpenAI-compatible endpoints.
Caching and operational ecosystem
The project includes radix-tree caching capabilities and related ecosystem projects such as a Kubernetes operator for serving, GPU scheduling, and model lifecycle management.
Use Cases
“Deploying open models for applications”
Developers can install the framework, launch a server against a supported language or multimodal model, and connect an application through OpenAI-compatible API endpoints.
“Serving long-context and agentic workloads”
Teams evaluating supported reasoning, coding, and agent-oriented models can use the serving engine and its parallelism, caching, and decoding optimizations as part of a model-serving stack.
“Multimodal and generative model inference”
The model catalog covers vision-language, OCR, image, video, audio, and diffusion models, making the framework applicable to supported non-text inference workloads as well as standard LLM serving.
“Distributed infrastructure operations”
Organizations running model services across clusters can use the framework's distributed serving capabilities and explore the surrounding ecosystem, including OME, a Kubernetes operator for LLM serving, GPU scheduling, and model lifecycle management.
“Research and reinforcement-learning systems”
Related projects in the ecosystem support speculative-decoding draft-model training, reinforcement-learning data generation, and large-scale MoE post-training workflows built around SGLang.
Frequently Asked Questions
How do you get started with SGLang?
The site describes a three-step workflow: install SGLang with pip or Docker, launch the server with a selected model, and query it through an OpenAI-compatible API. Platform-specific package commands depend on the chosen runtime and hardware.
What types of models does SGLang support?
The catalog includes LLMs, mixture-of-experts models, vision-language and other multimodal models, diffusion models, OCR models, and speech-related models. Examples of listed model families include DeepSeek, Qwen, Llama, Mistral, and GLM.
Which hardware platforms are supported?
The site lists NVIDIA GPUs, AMD GPUs, CPU servers, TPUs, Ascend NPUs, and XPUs. Actual compatibility depends on the model, backend, and platform-specific support.
Can applications use an OpenAI-style interface?
Yes. The documented basic workflow uses standard OpenAI-compatible endpoints after the SGLang server is launched.
Is SGLang open source?
Yes. The site presents SGLang as an open-source serving framework and links to its codebase and community resources.
Quick Facts
- Category
- Developer Tool; AI Infrastructure
- Primary function
- Serving inference for LLM and multimodal models
- Deployment range
- Single GPU to distributed clusters
- API style
- OpenAI-compatible endpoints
- Supported hardware
- NVIDIA GPUs, AMD GPUs, CPUs, TPUs, Ascend NPUs, and XPUs
- Installation options
- pip or Docker
SGLang Alternatives
vLLM
vllm.ai
vLLM is an inference and serving engine for large language models, designed for high-throughput, memory-efficient deployment. It helps developers and infrastructure teams run open-source models through an OpenAI-compatible API across supported compute platforms.
SambaNova Cloud
cloud.sambanova.ai
SambaNova Cloud is an AI inference platform that provides API access to open-source language and vision models. Developers can use its OpenAI-compatible API, playground, and model catalog to build and test AI-powered applications.
Chutes
chutes.ai
Chutes is a decentralized, serverless AI inference platform for deploying, running, and scaling open-source models. Developers can access hosted models through an API or deploy private custom workloads on confidential GPU capacity.
SambaNova
sambanova.ai
SambaNova is an AI inference platform for developers, enterprises, and infrastructure operators running large open-source models and agentic workloads. It combines cloud services with deployable hardware and software for cloud, on-premises, hybrid, and air-gapped environments.
Parasail
parasail.io
Parasail is an inference cloud for AI-native startups that provides access to open and frontier models through an OpenAI-compatible API. It offers serverless, elastic, dedicated, and batch deployment options with per-token or GPU-based pricing.
FriendliAI
friendli.ai
FriendliAI is an inference cloud for deploying frontier open-weight and custom AI models in production. It offers serverless Model APIs, dedicated GPU endpoints, and BYOG options for agent, multimodal, and other high-throughput workloads.