Ollama is an open-source platform for running large language models (LLMs) locally on macOS, Linux, and Windows. It lets you download, manage, customize, and run models such as Llama 4, Qwen 3, Gemma 3, Phi-4, DeepSeek-R1, Mistral, and many others, without relying on cloud services. Ollama is free and open-source under the MIT License.

Do I need an internet connection to use Ollama?

Only to download models. Once downloaded, models run entirely offline, giving you full privacy and local control.

Can I use Ollama with my own models?

Yes, Ollama supports custom models via GGUF format and lets you tweak prompts and parameters through a Modelfile.

Features

  • Local-first and lightweight: Install and run LLMs via a simple CLI (ollama run ), leveraging your own CPU/GPU instead of cloud servers.
  • Extensible and developer-friendly: Integrates well with Docker, REST API, and libraries in Python and JavaScript.
  • Customizable models and prompts: Supports tweaking parameters, system messages (via "Modelfile"), and importing custom GGUF/Safetensors models.

What's New

  • OpenClaw channel setup: connect WhatsApp, Telegram, Discord, and other messaging channels through ollama launch openclaw
  • Enable flash attention for Gemma 4 on compatible GPUs
  • ollama launch openclaw now detects curl-based OpenCode installs at ~/.opencode/bin
  • Fix /save command for models with safetensors-based architectures

System Requirements

Platform & OS

  • macOS: Big Sur (11) or later
  • Linux: Ubuntu 18.04 / 20.04 / 22.04 or later
  • Windows: Full support via native installer or WSL 2

CPU

  • Minimum: Any modern x86-64 CPU supporting AVX2 (Intel/AMD)
  • Recommended: 11th-gen Intel or AMD Zen 4 with AVX512 and ideally DDR5 memory

Memory RAM

  • Minimum: 8GB
  • Recommended: 16GB

GPU (Optional, but boosts performance significantly)

  • Not required, but speeds inference dramatically
  • Minimum recommendation: Nvidia or AMD GPU with 8 GB VRAM for 7B – 13B models

For large models:

  • 30B model: 16 GB VRAM
  • 65B model: 32 GB VRAM
  • Very large models (70B+): 48 GB or more, possibly multi-GPU