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SGLang

Free

SGLang is a fast serving framework for large language models and vision language models.

FreeFree tier
Inputs: text, image, videoOutputs: text, image, video
Type
Open Source
Visit Website GitHub (38 stars)

About SGLang

SGLang is a high-performance, open-source serving framework designed for large language models (LLMs) and multimodal models (e.g., vision-language, video/image generation). It powers trillions of tokens daily and is recognized with an a16z Open Source AI Grant. SGLang provides day-0 support for latest open models like DeepSeek V3/R1, Nemotron, and MiMo, with optimizations for NVIDIA and AMD GPUs. The framework includes features such as a zero-overhead batch scheduler, cache-aware load balancer, structured outputs, speculative decoding (DFlash, Spec V2), and support for PD disaggregation, expert parallelism, and diffusion models. It also runs natively on TPUs via the SGLang-Jax backend and has been integrated into the PyTorch ecosystem.

Key Features

High-performance serving for LLMs and multimodal models
Day-0 support for latest open models (DeepSeek, Nemotron, MiMo, etc.)
Zero-overhead batch scheduler and cache-aware load balancer
Structured outputs and faster structured generation
Speculative decoding (DFlash, Spec V2) for improved inference speed
PD disaggregation and large-scale expert parallelism (EP)
Native TPU support via SGLang-Jax backend
Diffusion model acceleration for video and image generation
Optimizations for NVIDIA and AMD GPUs (e.g., GB200, MI300X)
Open-source with active community and weekly development meetings

Pros & Cons

Pros
  • Exceptional performance, powering trillions of tokens daily
  • Open-source and free with an active developer community
  • Day-0 support for new models, reducing deployment latency
  • Broad hardware compatibility (NVIDIA, AMD, TPU)
  • Rich set of optimization features (speculative decoding, EP, PD disaggregation)
  • Actively maintained with frequent releases and blog posts
Cons
  • Requires significant GPU resources for large-scale deployments
  • Steep learning curve for custom optimizations and configuration
  • Documentation may be spread across GitHub, blogs, and Slack
  • Primarily designed for advanced users; may not suit simple use cases

Best For

Deploying and scaling large language models in productionServing multimodal models (vision-language, image/video generation)Running inference on high-throughput API endpointsBuilding AI applications with structured output requirementsResearch and experimentation with cutting-edge LLM architecturesAccelerating diffusion-based video and image generation pipelines

FAQ

What is SGLang?
SGLang is a fast, open-source serving framework for large language models (LLMs) and multimodal models, designed to deliver high performance and day-0 support for the latest models.
Which models does SGLang support?
SGLang supports a wide range of open models including DeepSeek V3/R1, Nemotron series, MiMo, Mistral Large, LLaDA, and many others. It also supports vision-language models and diffusion models for image/video generation.
What hardware platforms are supported?
SGLang runs on NVIDIA GPUs (e.g., GB200, H100), AMD Instinct GPUs (MI300X), and TPUs via the SGLang-Jax backend.
Is SGLang free?
Yes, SGLang is completely open source and free to use under its license.
Does SGLang support structured outputs?
Yes, SGLang includes support for structured outputs and faster generation of structured data.