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Get up and running with Llama 3.3, DeepSeek-R1, Phi-4, Gemma 3, and other large language models.

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Ollama

Get up and running with large language models.

macOS

Download

Windows

Download

Linux

curl -fsSL https://ollama.com/install.sh| sh

Manual install instructions

Docker

The officialOllama Docker imageollama/ollama is available on Docker Hub.

Libraries

Community

Quickstart

To run and chat withLlama 3.2:

ollama run llama3.2

Model library

Ollama supports a list of models available onollama.com/library

Here are some example models that can be downloaded:

ModelParametersSizeDownload
Gemma 31B815MBollama run gemma3:1b
Gemma 34B3.3GBollama run gemma3
Gemma 312B8.1GBollama run gemma3:12b
Gemma 327B17GBollama run gemma3:27b
QwQ32B20GBollama run qwq
DeepSeek-R17B4.7GBollama run deepseek-r1
DeepSeek-R1671B404GBollama run deepseek-r1:671b
Llama 3.370B43GBollama run llama3.3
Llama 3.23B2.0GBollama run llama3.2
Llama 3.21B1.3GBollama run llama3.2:1b
Llama 3.2 Vision11B7.9GBollama run llama3.2-vision
Llama 3.2 Vision90B55GBollama run llama3.2-vision:90b
Llama 3.18B4.7GBollama run llama3.1
Llama 3.1405B231GBollama run llama3.1:405b
Phi 414B9.1GBollama run phi4
Phi 4 Mini3.8B2.5GBollama run phi4-mini
Mistral7B4.1GBollama run mistral
Moondream 21.4B829MBollama run moondream
Neural Chat7B4.1GBollama run neural-chat
Starling7B4.1GBollama run starling-lm
Code Llama7B3.8GBollama run codellama
Llama 2 Uncensored7B3.8GBollama run llama2-uncensored
LLaVA7B4.5GBollama run llava
Granite-3.28B4.9GBollama run granite3.2

Note

You should have at least 8 GB of RAM available to run the 7B models, 16 GB to run the 13B models, and 32 GB to run the 33B models.

Customize a model

Import from GGUF

Ollama supports importing GGUF models in the Modelfile:

  1. Create a file namedModelfile, with aFROM instruction with the local filepath to the model you want to import.

    FROM ./vicuna-33b.Q4_0.gguf

  2. Create the model in Ollama

    ollama create example -f Modelfile

  3. Run the model

    ollama run example

Import from Safetensors

See theguide on importing models for more information.

Customize a prompt

Models from the Ollama library can be customized with a prompt. For example, to customize thellama3.2 model:

ollama pull llama3.2

Create aModelfile:

FROM llama3.2# set the temperature to 1 [higher is more creative, lower is more coherent]PARAMETER temperature 1# set the system messageSYSTEM """You are Mario from Super Mario Bros. Answer as Mario, the assistant, only."""

Next, create and run the model:

ollama create mario -f ./Modelfileollama run mario>>> hiHello! It's your friend Mario.

For more information on working with a Modelfile, see theModelfile documentation.

CLI Reference

Create a model

ollama create is used to create a model from a Modelfile.

ollama create mymodel -f ./Modelfile

Pull a model

ollama pull llama3.2

This command can also be used to update a local model. Only the diff will be pulled.

Remove a model

ollama rm llama3.2

Copy a model

ollama cp llama3.2 my-model

Multiline input

For multiline input, you can wrap text with""":

>>> """Hello,... world!... """I'm a basic program that prints the famous "Hello, world!" message to the console.

Multimodal models

ollama run llava "What's in this image? /Users/jmorgan/Desktop/smile.png"

Output: The image features a yellow smiley face, which is likely the central focus of the picture.

Pass the prompt as an argument

ollama run llama3.2"Summarize this file:$(cat README.md)"

Output: Ollama is a lightweight, extensible framework for building and running language models on the local machine. It provides a simple API for creating, running, and managing models, as well as a library of pre-built models that can be easily used in a variety of applications.

Show model information

ollama show llama3.2

List models on your computer

ollama list

List which models are currently loaded

ollama ps

Stop a model which is currently running

ollama stop llama3.2

Start Ollama

ollama serve is used when you want to start ollama without running the desktop application.

Building

See thedeveloper guide

Running local builds

Next, start the server:

./ollama serve

Finally, in a separate shell, run a model:

./ollama run llama3.2

REST API

Ollama has a REST API for running and managing models.

Generate a response

curl http://localhost:11434/api/generate -d'{  "model": "llama3.2",  "prompt":"Why is the sky blue?"}'

Chat with a model

curl http://localhost:11434/api/chat -d'{  "model": "llama3.2",  "messages": [    { "role": "user", "content": "why is the sky blue?" }  ]}'

See theAPI documentation for all endpoints.

Community Integrations

Web & Desktop

Cloud

Terminal

Apple Vision Pro

  • SwiftChat (Cross-platform AI chat app supporting Apple Vision Pro via "Designed for iPad")
  • Enchanted

Database

  • pgai - PostgreSQL as a vector database (Create and search embeddings from Ollama models using pgvector)
  • MindsDB (Connects Ollama models with nearly 200 data platforms and apps)
  • chromem-go withexample
  • Kangaroo (AI-powered SQL client and admin tool for popular databases)

Package managers

Libraries

Mobile

  • SwiftChat (Lightning-fast Cross-platform AI chat app with native UI for Android, iOS and iPad)
  • Enchanted
  • Maid
  • Ollama App (Modern and easy-to-use multi-platform client for Ollama)
  • ConfiChat (Lightweight, standalone, multi-platform, and privacy focused LLM chat interface with optional encryption)
  • Ollama Android Chat (No need for Termux, start the Ollama service with one click on an Android device)
  • Reins (Easily tweak parameters, customize system prompts per chat, and enhance your AI experiments with reasoning model support.)

Extensions & Plugins

Supported backends

  • llama.cpp project founded by Georgi Gerganov.

Observability

  • Opik is an open-source platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and production-ready dashboards. Opik supports native intergration to Ollama.
  • Lunary is the leading open-source LLM observability platform. It provides a variety of enterprise-grade features such as real-time analytics, prompt templates management, PII masking, and comprehensive agent tracing.
  • OpenLIT is an OpenTelemetry-native tool for monitoring Ollama Applications & GPUs using traces and metrics.
  • HoneyHive is an AI observability and evaluation platform for AI agents. Use HoneyHive to evaluate agent performance, interrogate failures, and monitor quality in production.
  • Langfuse is an open source LLM observability platform that enables teams to collaboratively monitor, evaluate and debug AI applications.
  • MLflow Tracing is an open source LLM observability tool with a convenient API to log and visualize traces, making it easy to debug and evaluate GenAI applications.
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