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Open-source engine for any M-series Mac

Running Gemma 4 26B in 2 GB RAM

Show HN — 779 points

github.com/drumih/turbo-fieldfare

02

The Breakthrough

Making large models accessible on consumer hardware

26B
Parameters running in just 2 GB RAM
Previously required 50+ GB for full precision models

Key Innovations

memory

Extreme Compression

Advanced quantization that reduces memory footprint dramatically

speed

M-Series Optimized

Leverages Apple Silicon's unified memory architecture

lock_open

Open Source

Fully open-source engine available on GitHub

devices

Any Mac

Runs on any M-series Mac, not just Pro machines

05

Technical Highlights

How it achieves unprecedented efficiency

Traditional vs. Turbo-Fieldfare

Traditional Approach
  • 50+ GB RAM required
  • Server-grade hardware
  • Cloud dependency
  • High operational costs
Turbo-Fieldfare Innovation
  • 2 GB RAM only
  • Consumer M-series Mac
  • Fully local processing
  • Zero inference costs

Why This Matters

  • 01
    Democratization Anyone with an M-series Mac can run state-of-the-art models locally
  • 02
    Privacy First All processing happens locally — no data leaves your device
  • 03
    Cost Effective No cloud API costs, no subscription fees, run unlimited queries
  • 04
    Developer Friendly Open-source code allows customization and learning

Try It Today

Star the repo and start experimenting with local LLMs

github.com/drumih/turbo-fieldfare
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