Free PDF — 12+ pages

The Local LLM Hardware Cheatsheet

2026 Edition · For Developers Who'd Rather Own Their Stack

Every hardware tier from Raspberry Pi to M5 Max. VRAM math, Ollama setup commands, model recommendations, and real benchmarks. The reference I wished I had when I started.

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Hardware Tiers at a Glance From $80 Raspberry Pi to $6,500 M5 Max — what each tier can and can't run, with real 2026 prices
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The VRAM Math Model size × quantization → VRAM needed. Stop guessing, start sizing correctly the first time
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Setup in 60 Seconds Ollama install, model pull, context-window tuning — copy-paste straight into your terminal
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Cost vs Cloud APIs Real break-even math: when does local hardware pay for itself vs Claude/GPT-4? Spoiler: faster than you think.

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What's Inside

Hardware

  • Hardware Tiers — Raspberry Pi to M5 Max with 2026 prices
  • NVIDIA vs AMD vs Apple Silicon — choose your lane
  • VRAM and memory math — never overspec or underspec again
  • Performance benchmarks — real tokens/sec by hardware × model

Setup

  • Quick-start: Ollama in 60 seconds
  • Model recommendations by use case
  • Common setup gotchas (and fixes)
  • Context-window tuning, quantization choices

Strategy

  • Cost comparison vs cloud APIs (real break-even math)
  • Privacy and compliance wins for healthcare, finance, legal
  • When local wins — and when cloud is still the right call
  • Curated reading path through the deep-dive blog posts

Reference

  • Quantization cheat-table — Q4 vs Q5 vs Q8 vs FP16
  • Recommended models per hardware tier
  • Use-case → model map (coding, RAG, chat, fine-tuning)
  • Links to all 16 blog posts in the local-LLM cluster