SGLang

Health v2 · maintenance only

Adoption not part of the score

37257 stars · 9654 forks observed · 2026-10-18

What it is AI-extracted, prompt v1, taxonomy v1, 2026-09-28, confidence 0.97

SGLang is an open‑source, high‑performance serving framework for large language models and vision‑language models. It provides an OpenAI‑compatible API, advanced scheduling, and GPU‑accelerated kernels for low‑latency, high‑throughput inference.

Use cases

• Deploy a LLM with OpenAI‑compatible endpoints for chat or completion services
• Serve multimodal models (text‑to‑image, video generation) at scale on a single GPU or a cluster
• Run speculative decoding and prefix caching to maximize throughput on NVIDIA or AMD GPUs
• Integrate custom inference pipelines into existing Python applications via a CLI or library calls
• Provide low‑latency inference for production workloads across heterogeneous hardware (GPU, CPU, TPU)

When to choose

• You need production‑grade, low‑latency serving for LLMs or multimodal models
• Your deployment targets CUDA‑capable GPUs, AMD GPUs, or TPUs and you want hardware‑agnostic support
• You prefer an OpenAI‑compatible API to simplify client integration
• You require advanced optimizations like speculative decoding, prefix caching, or multi‑GPU parallelism

When to avoid

• You are looking for a pure training library rather than inference serving
• Your environment is limited to pure CPU without any GPU acceleration and you need high throughput
• You need a turnkey UI or dashboard; SGLang focuses on the backend serving layer

Facets

library · maturity active

api-framework http-server middleware cli artificial-intelligence large-language-models windows python high-performance openai-compatible speculative-decoding multi-gpu cuda rocm tpu distributed-serving multimodal inference linux macos docker gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="sgl-project/sglang")