Local AI On Apple Silicon uses 7X Less RAM

Local AI on Apple Silicon now runs a 26B model in ~2GB RAM using SSD streaming, here's how the 7x memory drop actually works A 26-billion-parameter model that takes up 15GB on disk now runs on a MacBook using roughly 2GB of active memory. This video breaks down how local AI on Apple Silicon got here, tracing the trick back to Apple's own 2023 research paper, LLM in a Flash, which proved a Mac could run models twice the size of its available DRAM by streaming weights off the drive instead of loading everything into RAM at once, and then explains why nobody actually shipped that idea until now. The answer is Turbo Fieldfare, an open-source Swift and Metal project that finally built Apple's blueprint, but the real story is where the extra savings came from: Gemma 4's Mixture-of-Experts architecture, which activates only 8 of 128 expert sections per token, leaving roughly 85% of the network idle and safe to leave parked on flash storage. You'll see why this only works because of Apple's unified memory architecture (no CPU-to-GPU copy tollbooth), why a dense model like Qwen3.6-27B can't use this trick at all, why memory bandwidth, not GPU cores or Neural Engine TOPS, is the real predictor of speed, and why identical MacBooks can perform differently due to NAND lottery and fanless thermal throttling. Real benchmarks from llama.cpp on M2 Air and M2 Max are covered, along with what the Chapters: 0:00 The paper that promised twice 1:34 Why nobody could just do it 2:26 Somebody finally built it 3:21 What the drive trick actually buys 4:24 The model changed shape 5:26 Why it has to be a Mac 6:37 What you actually get out of it 7:50 The drive is the engine now 8:46 The lottery inside identical laptops 9:47 The laptop with no fan 10:45 The number nobody reads 11:54 The models it can't help 13:02 What the number is measured against 14:02 Who the seven times belongs to About The Stack The Stack helps you build with AI. Each video takes one tool, model, or workflow and shows how it works in a few focused minutes, with the real benchmarks and real costs. We go deep on Claude Code and Cursor for AI coding, AI agents and MCP servers, the open-source AI tools and GitHub repos most people miss, RAG and vector search, fine-tuning, and running local LLMs on your own machine with Ollama and LM Studio. We compare models like ChatGPT and Claude, test AI automation with Zapier, Make, and n8n, and flag the tools that actually ship. Subscribe for new breakdowns: https://www.youtube.com/@the-stack-ai?sub_confirmation=1 #local ai" claim is actually measured against. For builders curious about running large open models locally, anyone comparing LM Studio or Open WebUI setups, and anyone trying to understand transformer and MoE architecture trends before buying their next Mac. #hashtags