Ollama Review

One command is the entire onboarding. Before Ollama, running an open weight model meant Python environments, quantisation formats and a lost weekend. After it, it meant typing ollama run and waiting ninety seconds for a download. The MIT licensed runtime wraps the weights, a chat CLI, a background server and an OpenAI compatible endpoint into a single install on macOS, Linux and Windows, which is why so much other software simply assumes it is there and points at localhost.

Judge it on what it enables, because it is not a model and has no opinion about your prompt. Everything in the critical thinking and coding columns belongs to whatever you pulled, and the honest position in 2026 is that a model small enough to fit a consumer GPU still writes code a tier or two below what twenty dollars a month buys hosted. What Ollama does own is privacy and marginal cost. Nothing is logged, nothing is trained on, the request after the first one is free, and that is the whole argument for regulated data, bulk jobs and working on a plane.

The trap for newcomers is context. Ollama applies a modest default window regardless of what the model can actually do, so a first attempt at a long document gets silently truncated and the model takes the blame. Raise it on purpose. The company added paid cloud tiers at the end of August for people who want models larger than their hardware allows, starting free and reaching twenty dollars a month for Pro, but the local product stays free and stays the point.