How fast are LLM inference engines anyway? — Charles Frye, Modal
Jun 27, 2025 · 16:07
Charles Frye presents benchmarks from hundreds of runs on Modal comparing open-source inference engines VLM, SGLang, and TensorRTLM across models like Qwen 3 and Gemma 27B, arguing open weights models have caught up to proprietary ones, making self-hosting viable. He shows, for example, that Qwen 3 (MoE) on VLM achieves ~1 request/sec with 128 input tokens and 1024 output tokens, while switching to a RAG-like workload (1024 in, 128 out) yields a 4x throughput improvement. Frye warns that optimizing for context over reasoning can improve latency without sacrificing quality, and notes that the engines' out-of-the-box performance varies by model—e.g., SGLang underperforms VLM on Gemma due to less optimization. He also highlights the gap between prefill (parallel) and decode (autoregressive) speeds, which a rationalist would expect from transformer architecture. The benchmarks, available at modal.com/llmalmanac, aim to help engineers choose hardware and engines, with contributions welcome for optimized configs like TensorRTLM's knobs.
