SGLang has rapidly emerged as one of the most popular LLM serving engines, amassing over 25,000 GitHub stars through its focus on serving performance and developer experience. Developed by the SGLang project at UC Berkeley, it introduces RadixAttention — a technique that automatically reuses KV cache across requests sharing common prefixes, significantly improving throughput for applications with system prompts, few-shot examples, or multi-turn conversations. This approach eliminates redundant computation that other serving engines perform repeatedly.
For structured output generation like JSON schemas and function calling, SGLang uses compressed finite state machines that constrain token generation without the latency overhead of traditional constrained decoding. The engine supports continuous batching for optimal GPU utilization, tensor parallelism for distributing large models across GPUs, and speculative decoding for reduced latency. It handles both text-only and vision-language models including LLaMA, Mistral, Qwen, Gemma, LLaVA, and many more architectures.
SGLang provides an OpenAI-compatible API server for easy integration with existing applications, along with a Python frontend for programmatic control over generation. It runs on NVIDIA and AMD GPUs, and the project maintains active development with frequent releases adding new model support and performance optimizations. As an Apache 2.0 licensed project, SGLang represents a strong alternative to vLLM with particular advantages in structured generation and prefix-heavy workloads.
