vLLM vs llama.cpp vs Ollama: What Happens When Your Model Doesn't Fit in 24GB VRAM

TL;DR

Benchmarked llama.cpp, Ollama, and vLLM across 5 models (1B to 116.8B params) on one RTX 3090 (24GB) + 128GB RAM home-lab box, priced through HomeLab Monitor. Inside 24GB, vLLM's continuous batching scales aggregate throughput 3.9x-5.4x from concurrency 1 to 8 (llama.cpp only manages 1.2x-1.9x, even with -np 8 explicitly set to match). Past 24GB — two models deliberately chosen to force RAM-spill — llama.cpp and Ollama both degrade to single-digit tok/s and keep generating. vLLM OOMs outright on both, at the same ~22.1-22.2GB-used / <700MB-free ceiling, regardless of quantization scheme. Sub-plot: llama.cpp's manually-tuned layer offload beats Ollama's automatic split by 37x on time-to-first-token during RAM-spill, while landing on nearly identical steady-state decode speed.

The roster

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