Ollama — AI server management

A tool for running and serving large language models locally with a single command

Basic Information

What is this program?

Ollama is a runtime for running large language models in a local environment. Just as Docker manages container images by name, Ollama downloads models by name and runs them immediately. When you enter something like ollama run llama3.1 on the command line, the model is prepared and you can start a conversation.

The running model is also exposed through an OpenAI-compatible REST API (default port 11434). Existing applications that used the OpenAI API can switch to a local model just by changing the base URL. This is useful for organizations where privacy is important or in offline environments.

What problems does it solve?

• Situations where you need to perform internal document analysis or code writing without sending data to an external LLM API
• Situations where you need to quickly swap and compare multiple test models in a personal development environment
• Situations where you need to implement AI features on a server with network restrictions

Key features

Model management

Download and manage various open-weight models from the model registry

• Use case: Download the models you need at once and manage the list
• Result: Check installed models with ollama list, add more with ollama pull
• Limitations: Each model requires disk space

Single-command execution

Load a model and provide an interactive interface without separate configuration

• Use case: Start a test chatbot or code explainer immediately
• Result: Can chat directly in the terminal
• Limitations: Loading time varies depending on hardware specifications

OpenAI-compatible API

Expose REST endpoints in the form of the OpenAI API

• Use case: Use a local model by changing only the base URL of an existing app
• Result: Minimize client code changes
• Limitations: Does not support every feature of the OpenAI API

Accelerator support

Utilize various accelerators such as NVIDIA CUDA, AMD ROCm, and Apple Silicon MLX

• Use case: Improve inference speed when a GPU is available
• Result: Automatically selects the backend that matches the hardware
• Limitations: Large models are slow in CPU-only environments

Installation and execution

Linux (official install script)

Install with the official script. Download the script with curl and run it.

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

macOS / Windows

Run the installer downloaded from the website. Homebrew is also supported.

brew install ollama

Docker

Run in an isolated environment using the official container image.

docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama

Real-world usage examples

Run your first model

Download a lightweight model and start an interactive session.

ollama run llama3.2

Check the model list

Check installed models and their sizes.

ollama list

Call inference via API

Send a chat request to the OpenAI-compatible endpoint.

curl http://localhost:11434/v1/chat/completions -d '{"model":"llama3.2","messages":[{"role":"user","content":"ping"}]}'

Advantages and limitations

Advantages

• Installation and model execution are completed with a single command
• Easy integration with existing tools through the OpenAI-compatible API
• Supports various accelerators including NVIDIA, AMD, and Apple Silicon
• Free to use and distribute under the MIT license

Points to consider before use

• With many concurrent requests, GPU memory usage efficiency is lower than vLLM
• Large models are difficult to run without enough RAM/VRAM
• Production multi-user serving requires separate optimization

Comparison with similar programs

Official resources and further learning

• Official website — https://ollama.com/
• Official documentation / knowledge base — https://docs.ollama.com/
• Official download — https://ollama.com/download
• Release notes — https://github.com/ollama/ollama/releases
• Source / development resources — https://github.com/ollama/ollama

Version and supported environment information was checked on the official site as of 2026-10-18. Installation commands may vary depending on your distribution and version, so please also refer to the official installation documentation.

Actual execution screen

Synthetic example — not an actual execution result. The screen is a synthetic example and not an actual execution result.

!Ollama model list and run example

A server in actual operation

Source (HTML): https://games.aidebatehub.com/programs/apps/aiserver/ollama.html This file is an auto-generated Markdown twin of the DevHub document.