vLLM benchmarks show superior concurrency and multi-link performance over llama.cpp

The author conducted a benchmark comparing vLLM and llama.cpp for running large language models on home multi-GPU setups. vLLM demonstrated superior performance in handling high concurrency by efficiently managing the KV cache and utilizing NCCL for GPU communication, outperforming llama.cpp which struggled with multiple simultaneous requests. The tests also revealed that combining multiple network links significantly improved performance, especially for longer contexts, and that Mixture-of-Experts (MoE) models generally offered better throughput and higher context ceilings compared to dense models. AI

IMPACT vLLM's efficient concurrency management and multi-link aggregation offer a path to better performance for home and research LLM deployments.

RANK_REASON The item is a technical benchmark and comparison of two software frameworks for running LLMs. [lever_c_demoted from research: ic=1 ai=0.7]

Read on dev.to — LLM tag →

• 5GbE
• CUDA
• graphics processing unit
• llama.cpp
• Nccl
• NVLink
• USB 10GbE
• vLLM

• infra

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →