2026 Technical Benchmark
vLLM vs SGLang: High-Throughput GPU Inference Engine Battle
Comparing PagedAttention dynamic memory caching with structured RadixAttention execution for LLM hosting.
| Metric | vLLM Engine | SGLang |
|---|---|---|
| Core Memory Engine | PagedAttention dynamic KV-cache | RadixAttention tree KV-cache sharing |
| Multi-Turn Latency | High throughput batched serving | Substantially faster multi-turn prompts |
| Model Support | Virtually all open-source LLMs | Extensive HuggingFace model library |
| Production Adoption | Standard in major cloud platforms | Rapidly growing frontier tech stack |
| Best Suited For | General high-scale API gateways | Complex multi-step agent pipelines |
◈ Architecture Recommendation
SGLang delivers faster multi-turn agentic throughput; vLLM remains the most mature enterprise inference server.