vLLM, TGI and Ollama Compared on GPU Memory, Throughput and Batching

A benchmark comparison examines three LLM inference systems: vLLM, a throughput-focused engine built on PagedAttention and continuous batching, TGI from Hugging Face, and Ollama, a local llama.cpp-based runner. On Llama-3-8B at fp16, vLLM reached about 3,480 tokens per second across 65 concurrent requests, compared with roughly 2,910 for TGI and only 96 for Ollama. The analysis attributes vLLM's memory efficiency to paging the key-value cache into small blocks, which sharply reduces fragmentation compared with naive per-sequence buffers. The author recommends vLLM for dedicated GPU fleets serving more than 10 requests per second, TGI where enterprise support and native model hub integration matter, and Ollama only for local development or single-user use.