⚡ TL;DR — 30-Second Verdict
Choose vLLM for general-purpose high-throughput LLM serving with the broadest model support and most mature ecosystem. Choose SGLang if your workload involves multi-turn conversations, structured outputs, or complex LLM programs where RadixAttention's prefix caching provides significant speedups. SGLang is newer but has shown impressive benchmark results for specific use cases.
Quick Comparison
| Feature | vLLM | SGLang |
|---|---|---|
| KV cache algorithm | PagedAttention | RadixAttention (prefix caching) |
| Multi-turn speed | Standard performance | Up to 5x faster via prefix reuse |
| Model support | Very broad (100+ models) | Growing (major models supported) |
| Structured output | Via guided decoding | Native SGLang language support |
| Ecosystem maturity | Mature, widely deployed | Newer, rapidly evolving |
| OpenAI API compat | Full | Full |
| Multi-GPU | Tensor + pipeline parallelism | Tensor parallelism |
What Is vLLM?
vLLM is the correct answer for production LLM API serving on GPU. The PagedAttention innovation delivers 2–24x throughput over naive HuggingFace inference, and the OpenAI-compatible API means zero client-side changes when migrating from the OpenAI API. If you're deploying any model larger than 7B in production, evaluate vLLM first. The one real limitation: it's GPU-only and requires CUDA.
— AI Nav Editorial Team on vLLM
What Is SGLang?
SGLang is a focused tool that does one thing well. A solid choice for local LLM deployment when you want complete data privacy. The setup takes more effort than cloud APIs, but the zero-cost inference and offline capability make it worthwhile for teams with privacy requirements or high inference volume.
— AI Nav Editorial Team on SGLang