Running LLMs on Apple Silicon Is Getting Serious — Hypura Scheduler (194pts on HN)

A new project just hit Hacker News at 194+ points: Hypura — a storage-tier-aware LLM inference scheduler specifically for Apple Silicon. This is significant because it addresses the biggest limitation of running LLMs locally on Mac: memory management. The Problem Running a 70B parameter model on a MacBook Pro: Model RAM Needed M3 Max (96GB) M4 Ultra (192GB) Llama 3 8B 8GB ✅ Fast ✅ Fast Llama 3 70B 40GB ⚠️ Slow (swap) ✅ Fast Mixtral 8x22B 88GB ❌ Won't fit ⚠️ Tight Llama 3 405B 200GB+ ❌ ❌ Apple's unified memory is great, but when models exceed available RAM, inference falls off a cliff. What Hypura Does Hypura is a scheduler that's aware of Apple Silicon's storage tiers — it intelligently manages which model layers live in: Unified Memory (fastest) SSD swap (slower but huge) Compressed memory (middle ground) This means you can run larger models than your RAM should allow, with better performance than naive swap. Why This Matters for Developers 1. Local LLM development gets more practical