Running large language models at home with Ollama
10 months ago
- #LLM
- #Ollama
- #Quantization
- Running large language models (LLMs) locally has become feasible due to quantization and Ollama.
- Quantization reduces model size and speeds up calculations by converting weights to lower precision.
- Benefits of local LLMs include privacy, no usage limits, and freedom to use uncensored models.
- Ollama supports various hardware setups, from 8GB GPUs (RTX 3060) to 48GB setups (2 × RTX 3090).
- Installation involves setting up NVIDIA drivers, CUDA toolkit, and Ollama via script or Docker.
- Mistral 7B is a small model; more powerful models can be used with better hardware.
- Simon Willison's `llm` CLI enables tasks like summarizing logs, explaining code, and drafting templates.
- VS Code integration with Ollama offers AI-assisted coding via tools like Continue.
- Home Assistant supports Ollama for local conversation agents and smart-home control.
- Python scripting with Ollama's official client allows for custom applications and automation.
- Community releases offer uncensored models for specialized use cases like red-team testing.
- Ollama's ecosystem includes plugins for Vim, Emacs, Obsidian, and more.