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// 52d 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
52d ago
2026-04-05
PUBLISHED
53d ago
2026-04-04
RELEVANCE
8/ 10
AUTHOR
Obamos75