Intermediate โฑ๏ธ 8 min

๐ŸŽ“ Local AI Inference Guide

A comprehensive guide to running LLMs on your own hardware for privacy, speed, and customization

Local AI Inference Guide

Local Inference refers to running Large Language Models (LLMs) on your own hardware (laptop, desktop, or server) instead of relying on cloud APIs like OpenAI or Anthropic. This shift is driven by the need for privacy, offline access, and zero per-token costs.

Benefits of Going Local

  • Data Privacy: Your prompts and data never leave your machine. Ideal for sensitive documents.
  • Zero Latency/Cost: No waiting for rate limits or paying monthly subscriptions.
  • Customization: Run uncensored models or fine-tune models to your specific needs.
  • Offline Access: Use AI in the field or in secure environments without internet.
ToolDifficultyBest Forโ€ฆPlatform
OllamaBeginnerOne-line terminal commandsWin / Mac / Linux
LM StudioBeginnerVisual GUI & discovering modelsWin / Mac
llama.cppAdvancedMaximum performance & efficiencyCLI / All
Jan.aiIntermediateLocal alternative to ChatGPTDesktop
vLLMProHigh-throughput serving / APILinux / GPU

Hardware Cheat Sheet

1. Apple Silicon (MacBook M1/M2/M3)

The โ€œUnified Memoryโ€ architecture makes Macs a powerhouse for local AI.

  • 8GB RAM: 1B - 3B models (Phi-3, Gemma-2b).
  • 16GB+ RAM: 7B - 8B models (Llama 3, Mistral) run excellently.
  • 64GB+ RAM: Can run 70B models comfortably.

2. PC with NVIDIA GPU

Look for GPUs with at least 8GB VRAM (RTX 3060/4060).

  • RTX 3090/4090 (24GB): The king of local AI for consumers.

3. CPU Only

Possible with llama.cpp, but slow. Good for 3B-7B models if you are patient (1-3 tokens per second).

How to Get Started (The 5-Minute Path)

  1. Download Ollama from ollama.com.
  2. Run a model: Open your terminal and type ollama run llama3.
  3. Chat: The model will download (~4.7GB) and start an interactive chat session immediately.

Key Terminology

  • Quantization: Compressing models to fit in VRAM. (See Model Quantization)
  • VRAM: Video RAM on your GPU; the most important hardware metric. (See VRAM Requirements)
  • Context Window: How much โ€œmemoryโ€ the model has of the current chat.

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