Local LLMsImpact: High
Oleksandr Kuzmenko, EditorPublished 2 min readAI-assisted · editor-reviewed
Why it matters
It allows running massive models on low-memory hardware.
TL;DR
- 01Runs 26B model in ~2GB RAM budget.
- 02Uses Swift and Metal.
Key facts
- RAM Requirement
- ~2 GB
Architecture and SSD-Streaming
At each transformer layer, Metal computes attention and the router from resident weights. The CPU uses the router's top-8 expert IDs to plan against the layer's 16-slot LFU cache, then fills misses with bounded parallel pread calls into Metal-visible buffers. Metal computes the resident shared-expert branch while those reads run, then combines the shared and routed outputs.
#TurboFieldfare#Swift#Metal