SGLang
by Community
SGLang is a high-performance serving framework for large language models and multimodal models.
OSS
SGLang
Added 1 June 2026
Overview
SGLang is a Python framework for serving large language models and multimodal models with optimized performance. It provides APIs and tools to deploy, batch, and run inference on LLMs efficiently at scale.
Best for
Best for
Teams building production LLM services who need performance-optimized serving infrastructure
Use cases
- Deploying LLMs with low-latency inference serving
- Running multimodal model inference in production
- Batching and optimizing throughput for concurrent requests
Notes
SGLang is a Python framework for serving large language models and multimodal models with optimized performance. It provides APIs and tools to deploy, batch, and run inference on LLMs efficiently at scale.
28,885 stars on GitHub. Last updated 2026-06-01. Licensed Apache-2.0.
Use cases
- Deploying LLMs with low-latency inference serving
- Running multimodal model inference in production
- Batching and optimizing throughput for concurrent requests
Pros
- High-performance serving optimized for LLM inference
- Supports both language and multimodal models
- Active community project with substantial adoption (28k+ stars)
Cons
- Python-only, limiting integration in non-Python stacks
- Requires operational expertise to deploy and tune effectively
- Community-maintained, not backed by a commercial vendor
Indexed from awesome-llm and enriched against its public facts.
Pros
- High-performance serving optimized for LLM inference
- Supports both language and multimodal models
- Active community project with substantial adoption (28k+ stars)
Cons
- Python-only, limiting integration in non-Python stacks
- Requires operational expertise to deploy and tune effectively
- Community-maintained, not backed by a commercial vendor
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