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The Local LLM cheat sheet for your 16GB RAM device I pulled together a lineup of small models that can run comfortably on a Mac Mini or personal laptop while still leaving room for context without melting your machine. Models for Daily Use Qwen3.5 9B / GGUF / Q4_K_M Daily driver. General chat, drafting, research, translation. If you're keeping only one, keep this. DeepSeek-R1 Distill Qwen 7B / GGUF / Q4_K_M Reasoning engine. Math, logic, step-by-step problems. Slower, but worth it when you need actual thinking. Models for Specialty Work Qwen2.5 Coder 7B / GGUF / Q4_K_M Code specialist. Completions, refactors, debugging, repo Q&A. Better than a generalist when the task is code. Llama 3.1 8B / GGUF / Q4_K_M Long context worker. RAG, doc chat, codebase Q and A. The output isn't top tier, but the context is strong for its size. Phi-4 Mini Reasoning / GGUF / Q4_K_M Compact thinker. Logic, structured answers, math, and short coding bursts. Smaller context is the catch. Models for Efficiency Gemma 4 E4B / GGUF / Q4_K_M Light all-rounder. Writing, chat, light agents, structured output. Phi-3.5 Mini / GGUF / Q5_K_M Pocket sidekick. Summaries, extraction, background doc chat. Easy to pair with a bigger model. Qwen3.5 2B / GGUF / Q4_K_M Useful for summaries, tagging, rewrites, and lightweight sidekick work. Micro Models Qwen3.5 0.8B / GGUF / Q5_K_M Classification, keyword routing, binary decisions, triage. Gemma 4 E2B-it / GGUF / Q4_K_M Lightweight chat, quick Q and A, summaries, tiny agents. My personal choice for a single model is Qwen3.5 9B For two models use Qwen3.5 9B + Qwen2.5 Coder 7B for code, or Qwen3.5 9B + Phi-3.5 Mini for support tasks. Let me know in the comments your experience with these models, or any I have left out.
















