SGLang

SGLang is an open-source serving framework designed for high-performance inference of large language models and multimodal models.

SGLang provides a production-grade infrastructure for deploying large language and multimodal models across diverse hardware environments. The framework utilizes advanced optimizations like disaggregated prefill/decode, speculative decoding, and parallelisms to maximize GPU efficiency. It features a zero-overhead scheduler and optimized kernels to support reliable serving from single GPUs to distributed clusters. Users can launch servers with single commands and interact via standard OpenAI-compatible APIs. The system supports wide-ranging architectures, including various LLMs, diffusion models, and Mixture-of-Experts (MoE) models.

SGLang at a glance

Best for: Software engineer, Researcher
Runs on: API, self-hosted, open source
Pricing: Free
Released: January 2024
Category: AI coding tool
Website: sglang.io

Key features

OpenAI-compatible API endpoints
Disaggregated prefill and decode
Speculative decoding for improved throughput
Unified radix tree for efficient prefix caching
Multi-node context parallelism support
Hardware-optimized GPU kernels
Support for Mixture-of-Experts models

Use it for

Deploying large-scale LLMs for production inference
Serving multimodal models and diffusion models
Running agentic workflows with session-aware caching
Executing high-throughput reinforcement learning rollouts

Alternatives to SGLang

AirLLM — Runs massive 70B+ parameter models on small 4GB consumer GPUs.
LMDeploy — Accelerates inference with high-performance engines and quantization tools.
llm-d — Deploys production-grade LLM serving on existing hardware clusters.
vLLM — Maximizes throughput with PagedAttention and continuous batching.
Dlgo — Run LLM inference quickly in pure Go without Python or external runtimes.
LightLLM — It delivers high-speed inference and serving performance for large language models.
fal — Access 1,000+ generative models and high-performance serverless GPUs.
XTuner — High-efficiency training for 200B+ scale MoE models without expert parallelism.
GPUStack — Unifies AI model serving and GPU compute management across heterogeneous hardware.
Strata — Runs large AI models locally on consumer gaming PCs without server reliance.
Resin — Builds high-performance inference engines for complex models like DeepSeek and GLM.
Ray — It orchestrates distributed infrastructure for any AI workload at any scale.

All SGLang alternatives