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Explore 26 Billion Parameters. 2 GB of RAM with 20,821 views, 400 likes, and 8 comments. Experience the impact of this video content that has captured audience attention.

How can an 8GB M2 MacBook Air run Google's 26-billion parameter Gemma 4 model (14.3 GB on disk) with the memory meter pinned at only 2 GB of RAM? In this deep dive, Cloud Codes dissects TurboFieldfare, an open-source Swift and Metal runtime created by Andrey Mikhaylov that streams routed mixture-of-experts (MoE) weights directly off the SSD into unified memory. 🔔 Subscribe: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w?sub_confirmation=1 💙 Become a Member: https://www.youtube.com/channel/UC0DZj1PNa_Fp0MU6uPSKv5w/join 🐦 Twitter/X: https://x.com/cloud_codes 💬 Discord: https://discord.gg/4kJqEBMMf We analyze why traditional mmap demand paging crawls at 0.50 tokens/sec while explicit pread calls hit 4 to 6 tokens/sec on an M2 Air and 35 tokens/sec on an M5 Pro. We break down the 162.8ms decode step profile (83.1ms disk reads vs 55.6ms GPU queue), examine the 37-second prefill latency trade-off on 1,000-token prompts, audit the project's comparison against Apple MLX (which runs 2.5x faster but requires 15GB of GPU allocation), and evaluate the memory arbitrage behind $400 soldered RAM upgrades. If this breakdown helped you understand Apple Silicon unified memory, Metal compute kernels, and local LLM optimization, subscribe to Cloud Codes for a new infrastructure deep dive every week! Build, solve, deploy. 🔗 Repositories & Sources Mentioned: • TurboFieldfare GitHub Repository: https://github.com/drumih/turbo-fieldfare • Hacker News Show HN Discussion (Item 49098510): https://news.ycombinator.com/item?id=49098510 • Google Gemma 4 26B-A4B Model Documentation: https://ai.google.dev/gemma/docs/core/model_card_4 • Apple MLX Framework Repository: https://github.com/ml-explore/mlx • TrendForce DRAM Contract Price Surge Analysis: https://www.trendforce.com/presscenter/news/20260601-13070.html ⏱️ Video Chapters: 0:00 - Running a 26B Parameter Model in 2 GB of RAM 0:41 - Meet TurboFieldfare: SSD Streaming on Apple Silicon 1:05 - The Architecture: Gemma 4 MoE & 1.35 GB Common Core 2:52 - Why Memory Mapping Failed: mmap vs Explicit Reads 4:56 - The 162ms Decode Breakdown: Where Token Time Actually Goes 5:39 - The Prefill Bottleneck: 37 Seconds to First Token 6:09 - TurboFieldfare vs Apple MLX: The 2.5x Speed Trade-Off 7:21 - The Page Cache Reality: Why Free RAM Still Matters 8:12 - The RAM Arbitrage: Why Andrey Mikhaylov Built It 9:39 - The Final Verdict: When to Stream vs When to Buy MLX #localllm #applesilicon #macbookair #turbofieldfare #gemma4 #mlx #machinelearning #metal4 #cloudcodes #softwareengineering User Queries: how to run 26b llm on 8gb macbook air turbofieldfare gemma 4 26b ssd streaming metal turbofieldfare vs apple mlx benchmark comparison run 14gb model in 2gb ram apple silicon gemma 4 26b-a4b moe unified memory macos why apple silicon unified memory enables ssd streaming turbofieldfare pread vs mmap token speed macbook air 8gb local ai model inference andrey mikhaylov turbofieldfare hacker news ssd weight streaming local llm latency prefill

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