# Ollama

> Ollama local LLM deployment and management. Use for running LLMs locally. Use when this capability is needed.

- Skill: `tomevault-io/ollama` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds@latest add tomevault-io/ollama`
- Raw SKILL.md: https://api.skillmd.com/api/skills/tomevault-io/ollama/raw
- Safety review: pending (external: skill-scanner PASS, skillspector PASS)
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- Author: tomevault-io (https://skillmd.com/u/tomevault-io)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/tomevault-io/ollama

---


# Ollama

Ollama makes running LLMs locally as easy as `docker run`. 2025 updates include **Windows/AMD** support, **Multimodal** input, and Tool Calling.

## When to Use

- **Local Development**: Coding without wifi or API costs.
- **Privacy**: Processing sensitive documents on-device.
- **Integration**: Works with LangChain, LlamaIndex, and Obsidian natively.

## Core Concepts

### Modelfile

Docker-like file to define a custom model (System prompt + Base model).

```dockerfile
FROM llama3
SYSTEM You are Mario from Super Mario Bros.
```

### API

Ollama runs a local server (`localhost:11434`) compatible with OpenAI SDK.

## Best Practices (2025)

**Do**:

- **Use high-speed RAM**: Local LLM speed depends on memory bandwidth.
- **Use Quantized Models**: `q4_k_m` is the sweet spot for speed/quality balance.
- **Unload**: `ollama stop` when done to free VRAM for games/rendering.

**Don't**:

- **Don't expect GPT-4 level**: Smaller local models (8B) are smart but lack deep reasoning.

## References

- [Ollama Website](https://ollama.com/)

---
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<!-- tomevault:4.0:skill_md:2026-04-15 -->