Ollama — the tool that makes running large language models locally as simple as pulling a Docker image — is not just for powerful desktop machines. With the right Raspberry Pi model and a few sensible...
Ollama is best known for running large language models locally, but it also exposes a first-class embeddings API. Combined with a vector store, this lets you build semantic search engines, document qu...
The Ollama Python library is the official client for interacting with Ollama from Python code, but it isn’t the only way to call it — you can also use the OpenAI SDK for drop-in compatibility, o...
Running large language models locally with Ollama has never been more accessible, but choosing the right GPU can make the difference between a smooth experience and a frustrating one. This guide cuts ...
Running large language models locally has moved from a niche developer hobby to a practical option for privacy-conscious users, businesses, and developers who want full control over their AI stack. Tw...
Why Run Ollama Over a Network? By default, Ollama only listens on localhost:11434 — requests from other machines are rejected. Enabling network access lets you: Run Ollama on a powerful desktop or ser...
Why Ollama Runs Out of Memory Ollama loads model weights into RAM or VRAM to run inference. If the model is too large for the available memory, one of several things happens: the model offloads layers...
What Is an Ollama Modelfile? A Modelfile is a plain-text configuration file that defines how Ollama should build or customise a model. It works like a Dockerfile — you start from a base model, then la...
Running Ollama in Docker is one of the cleanest ways to self-host large language models on your own infrastructure. Whether you’re setting up a home lab, deploying to a production server, or jus...