REDDIT · REDDIT// 7d agoINFRASTRUCTURE

SGLang, TensorRT-LLM top H100 inference benchmarks

Developers are optimizing Llama 3.1 8B performance on NVIDIA H100 hardware, moving beyond standard vLLM deployments. SGLang and NVIDIA's TensorRT-LLM have emerged as the throughput leaders, offering up to 30% better performance for high-concurrency workloads and RAG applications.

// ANALYSIS

If you're paying for H100 compute, sticking with vLLM's defaults is essentially a performance tax.

–SGLang's RadixAttention provides massive gains for RAG by caching the KV cache across requests, avoiding redundant computation of shared system prompts.
–TensorRT-LLM achieves the highest raw throughput (~16,500 tok/s) but requires a complex compilation step that can hinder fast iteration.
–vLLM still maintains an edge in Time to First Token (TTFT) for low-concurrency interactive tasks, keeping it relevant for simple chat applications.
–For production-grade agents, SGLang’s orchestration efficiency and C++ backend optimizations make it the superior choice for scaling.

// TAGS

llminferencegpuh100sglangvllmtensorrt-llmopen-source

DISCOVERED

7d ago

2026-04-05

PUBLISHED

7d ago

2026-04-04

RELEVANCE

8/ 10

AUTHOR

Obamos75