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Ollama vs vLLM: Which Should You Use?

Ollama for personal use, vLLM for serving. Picking the wrong one will frustrate you.

Ollama is a llama.cpp wrapper with a great install experience. Single command pulls and runs a model. Works on macOS, Linux, Windows. Models stay in their CPU/GPU layout, you do not think about quantization or KV cache much. Throughput tops out around 1-4 concurrent users.

vLLM is a serving framework. PagedAttention, continuous batching, tensor parallelism, AWQ/GPTQ/FP8 support. Built for many concurrent users on NVIDIA. Slower for one user, much faster for ten.

If you are a single developer running a model on your laptop or desktop: Ollama. If you are exposing the model to a team or app: vLLM. The crossover point is around 5 concurrent users.