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Ollama

Run LLMs locally on your machine — Llama, Mistral, CodeLlama, and more. Ollama packages models for easy download and inference. No API costs, full privacy, offline. Great for development and when you can't send data to a cloud API.

Created by

Ollama team · Created Ollama in 2023

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When to use
  • Local development and prototyping.
  • Privacy-sensitive applications.
  • Offline AI apps.
  • When API costs are prohibitive.
  • Learning and experimentation.
When not to
  • When you need the best model quality (use cloud APIs).
  • Production at scale (cloud is more reliable).
  • When your hardware can't handle it.

Definition

Ollama is a tool for running LLMs locally. Download models (ollama pull llama3.2), run them (ollama run), or serve via API (localhost:11434). GPU-accelerated (CUDA, Metal). Model files are quantized for efficient local inference. Supports Llama, Mistral, Gemma, and more.

Why it exists

Cloud LLM APIs cost money and send your data to a third party. Ollama lets you run capable models locally — free, private, offline, and fast enough for many tasks.

How it works

Install Ollama. ollama pull <model> downloads it. ollama run <model> starts a chat. ollama serve exposes a local API (compatible with OpenAI's API format). Models are quantized (GGUF) for efficient CPU/GPU inference. Integrates with , , etc.

Scaling limits

Model quality is lower than GPT-4/Claude (local models are smaller). RAM/VRAM requirements (7B+ models need 8GB+). Slower than cloud APIs for large models. Limited to open-source models.

Cost shape

Free. You pay for your own hardware (GPU recommended for large models).

Alternatives

  • OpenAI/Anthropic API — Better quality, costs money, data leaves your machine.
  • llama.cpp — Lower-level, more control, less convenient.
  • vLLM — Production serving, more complex.

Failure modes

  • Model quality vs cloud.
  • Hardware requirements.
  • RAM/VRAM limits.
  • Slower inference.

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