SGLang NEW

SGLang is a high-performance serving framework for large language and multimodal models. It provides scalable, low-latency inference with high throughput across deployments from a single GPU to distributed clusters.

The engine supports a wide range of open models and diverse hardware platforms, enabling unified model and hardware flexibility.

Runtime optimizations include disaggregated prefill/decode, speculative decoding, parallelism strategies, a zero‑overhead scheduler, and optimized GPU kernels to reduce latency and increase throughput. Deployment is available via pip or Docker and exposes OpenAI-compatible API endpoints for integration.

The project includes tooling for multi-node scaling, monitoring, and community-driven extensions via GitHub and discussion channels.

Key Features

• Serving framework for large language and multimodal models
• Scalable inference from a single GPU to distributed clusters
• Unified support for a wide range of open models and diverse hardware platforms
• Runtime optimizations: disaggregated prefill/decode, speculative decoding, parallelism strategies, zero-overhead scheduler, and optimized GPU kernels
• Deployable via pip or Docker with OpenAI-compatible API endpoints and multi-node scaling/monitoring tooling

Use Cases

• Deploy a production-grade, low-latency conversational AI or customer support assistant using SGLang's OpenAI-compatible API endpoints and runtime optimizations—run on a single GPU or scale to multi-node clusters via pip or Docker for real-time responses and high throughput
• Serve multimodal applications like visual search, image captioning, or document understanding by leveraging SGLang's multimodal model serving and distributed GPU serving to process image+text inference at scale with minimal latency
• Build cost-effective batch and streaming inference pipelines for personalization, recommendation, or analytics by deploying SGLang across multi-node clusters, using speculative decoding and other optimizations to maximize throughput and reduce inference latency while keeping integration simple

Who is it for?

• Machine learning engineers
• Mlops engineers
• Infrastructure/platform engineers
• Devops engineers
• Ai/ml researchers
• Data scientists deploying production models
• Startups building llm- or multimodal-powered apps
• Enterprise engineering teams
• Cloud service providers and sres
• Gpu/cluster administrators
• Software engineers integrating models via openai-compatible apis
• Open-source contributors and community developers