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

Short summary

Benchmarked vLLM, llama.cpp, and Ollama on RTX 3090 with five models (1B–116.8B params). vLLM scaled throughput 3.9–5.4× with concurrency but crashed at ~22.2GB VRAM; llama.cpp and Ollama gracefully degraded with RAM spill. Manual offloading in llama.cpp beat Ollama's automatic split 37× on time-to-first-token.

• •vLLM achieves 3.9–5.4× throughput scaling with concurrency vs llama.cpp's 1.2–1.9×
• •vLLM crashes when models exceed ~22.2GB VRAM; llama.cpp and Ollama continue with degraded performance
• •Manual layer offloading in llama.cpp outperforms Ollama's automatic approach by 37× on first-token latency

Generated with AI, which can make mistakes.