What is it?
What it is
Ollama lets you run large language models locally on your machine. Pull models like Llama, Mistral, and Gemma with a single command and interact via CLI or API.
Why it exists
Developers needed a simple way to run LLMs locally without configuring CUDA, model weights, or inference servers manually.
Who should use it
Developers prototyping LLM apps, teams with data privacy requirements, or anyone wanting offline AI inference.
Who should avoid it
Production workloads needing multi-GPU clustering, or teams without sufficient RAM/VRAM for target models.
How it works
A quick walkthrough in plain English
How Ollama runs AI on your machine
Step 1 of 4
You send a prompt
Type in the terminal, use a chat UI, or call the API from your code.
Features
Advantages
- Dead simple local LLM setup
- No cloud API costs for development
- Data stays on your machine
- Works with LangChain, n8n, Open WebUI
Disadvantages
- Limited by local hardware
- Not designed for high-concurrency production
- Model quality depends on chosen weights
Installation
docker
docker run -d -v ollama:/root/.ollama -p 11434:11434 --gpus all ollama/ollama
native
# macOS brew install ollama ollama serve ollama pull llama3.2 ollama run llama3.2
FAQ
What hardware do I need?
7B models need ~8GB RAM. 13B needs 16GB+. GPU dramatically speeds inference.
Can I use Ollama with OpenAI SDK?
Yes. Point the OpenAI client to http://localhost:11434/v1 with any API key.
Ollama vs LM Studio?
Ollama is CLI/API focused. LM Studio has a GUI. Both run models locally.
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