Ollama – Run LLMs on your Mac
Hi HN A few folks and I have been working on this project for a couple weeks now. After previously working on the Docker project for a number of years (both on the container runtime and image registry side), the recent rise in open source language models made us think something similar needed to exist for large language models too. While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges. There are "base layers" (e.g. models like Llama 2), specific configuration to run correctly (parameters, temperature, context window sizes etc). There's…
In plain words
Ollama lets users run large language models locally on Mac computers. Built by former Docker developers, it handles the infrastructure challenges of running open-source models like Llama 2, managing configuration parameters, temperature settings, and context window sizes. The tool treats language models similarly to how Docker handles containers, providing a streamlined way to download, configure, and execute models without relying on external services. It is aimed at developers and researchers working with open-source language models.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
Hi HN A few folks and I have been working on this project for a couple weeks now. After previously working on the Docker project for a number of years (both on the container runtime and image registry side), the recent rise in open source language models made us think something similar needed to exist for large language models too. While not exactly the same as running linux containers, running LLMs shares quite a few of the same challenges. There are "base layers" (e.g. models like Llama 2), specific configuration to run correctly (parameters, temperature, context window sizes etc). There's also embeddings that a model can use at runtime to look up data – we don't support this yet but it's something we're looking at doing soon. It's an early project, and there's still lots to do!
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