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Local LLMs (Ollama, MLX, llama.cpp)

What it is

Tools and frameworks that allow running Large Language Models directly on your own hardware (Homelab, Workstation, Mac).

  • Ollama: The easiest way to get up and running with a simple CLI and API.
  • MLX: Apple's framework for high-performance AI on Apple Silicon.
  • llama.cpp: The foundational C++ library for running LLMs on consumer hardware.

What problem it solves

Provides 100% privacy, works offline, has no per-token costs, and allows for infinite experimentation without API limits.

Where it fits in the stack

LLM / Reasoning Engine (Self-hosted). Replaces cloud providers for tasks that don't require the massive scale of GPT-4.

Architecture overview

The model weights are downloaded and stored locally. Inference is performed using your local CPU/GPU/NPU.

Typical workflows

  • Local Development: Testing agent logic without incurring costs.
  • Sensitive Data Processing: Summarizing private documents or logs.
  • Always-on Low-latency Tasks: Simple classification or formatting that needs to happen fast and often.

Strengths

  • Privacy: No data leaves your machine.
  • Cost: Free (after purchasing the hardware).
  • Latency: No network round-trip to external APIs.
  • Customization: Use any open-weight model (Llama 3, Mistral, Qwen, etc.).

Limitations

  • Performance: Generally lower reasoning capability than the largest cloud models (GPT-4o/Claude 3.5).
  • Hardware Requirement: Requires significant RAM (especially for larger models) and GPU/NPU acceleration.
  • Maintenance: You are responsible for updating software and managing model files.

When to use it

  • For any task involving sensitive or personal data.
  • When you want to avoid recurring costs for high-volume, simpler tasks.
  • For local coding assistants (e.g., using llama-3-8b or deepseek-coder locally).

When not to use it

  • When you need the absolute highest reasoning performance available today.
  • If you lack dedicated hardware (GPU with 12GB+ VRAM or 16GB+ Mac Unified Memory).

Security considerations

  • Local API Access: By default, Ollama and others might listen on localhost. Be careful when exposing these to your local network.
  • Model Integrity: Download models from trusted sources (like the official Ollama library or reputable HuggingFace users).