Apple Silicon Can Run Local Al with Just 2GB of RAM Using Turbo Fieldfare
TL;DR
An analysis by Better Stack shows how Turbo Fieldfare runs a 26-billion-parameter model on Apple Silicon hardware with just 2 GB of RAM. This works through Gemma 4 and its mixture-of-experts architecture, where only part of the model capacity is active at any one time. For local AI, that shifts the practical hardware floor considerably. The figures currently come mainly from a Geeky Gadgets write-up and should be verified with your own measurements of speed, memory use, and model quality.
Nauti's Take
Small teams should test this on hardware they already own and measure latency, context length, power use, and output quality against a cloud model. The 2 GB figure is appealing, but it does not establish the total memory footprint or whether the model is fast enough for daily workflows.
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