2026 Technical Benchmark
SGLang vs TensorRT-LLM: Extreme Speed GPU Optimization Battle
Evaluating RadixAttention tree-caching against Nvidia kernel-level TensorRT optimizations.
| Metric | SGLang | Nvidia TensorRT-LLM |
|---|---|---|
| Primary Innovation | RadixAttention tree KV-cache reuse | Nvidia deep C++/CUDA kernel compiler |
| Multi-Turn Agents | Massive speedup on repeated context | Standard linear sequence processing |
| Setup Complexity | Simple Python & vLLM compatibility | Complex engine build and compilation |
| Hardware Target | Any modern GPU (AMD / Nvidia) | Exclusively Nvidia GPUs (Hopper / Ada) |
| Best Suited For | Multi-step autonomous agent apps | Ultra-low-latency enterprise chatbots |
◈ Architecture Recommendation
SGLang is faster for complex multi-turn LLM agent chains; TensorRT-LLM achieves peak raw single-stream token speed on Nvidia GPUs.