Llama models for Local AI

Choose and tune Llama‑class models for reasoning, drafting, and support work—running entirely on your Mac Mini or local server.

Reasoning models

Use larger Llama variants for deep analysis, planning, and complex chains of thought.

Everyday chat

Lighter Llama models handle day‑to‑day Q&A, drafting, and summarisation quickly and cheaply.

On‑prem control

All prompts and responses stay on hardware you own, giving you full control over data and model configuration.

Model strategy for teams

  • Pick 1–2 primary models for production work (reasoning + general chat).
  • Keep experimental models in a separate namespace for R&D.
  • Map models to use‑cases: support, drafting, analytics, engineering, etc.

How Llama fits the Local AI stack

  • Ollama hosts the models and streams tokens to Open WebUI, Obsidian, and other tools.
  • RAG adds your internal documents so Llama can answer questions based on your data.
  • Specialised prompt templates and system messages align behaviour with your workflows.

Hardware and tuning considerations

Hardware

  • Mac Mini with Apple Silicon (for example M4 Pro) and 96 GB RAM is the baseline.
  • Use quantised models to fit more context into available memory.
  • Reserve heavier models for scheduled, non‑interactive workloads.

Tuning

  • Craft system prompts that encode your organisation’s tone and rules.
  • Use test prompts and benchmark sets to compare model quality.
  • Iterate on temperature, max tokens, and stop sequences per use‑case.

Next steps

Need help choosing Llama models, planning capacity, or designing prompts and benchmarks? Book a Local AI session and we’ll design a model strategy for your team.

Talk about Llama models