Open-Source Engine Runs Gemma 4 26B on M-Series Macs with 2GB RAM
Summary
An open-source inference engine, TurboFieldfare, allows running the 4-bit quantized Gemma 4 26B model on M-series Macs using only 2GB of RAM by streaming model experts from SSD. It achieves 5-6 tokens/second on an M2 MacBook Air and 31-35 tokens/second on an M5 MacBook Pro, also offering an OpenAI-compatible local server.
Why it matters
Developers and researchers can now run powerful large language models locally on consumer-grade M-series Macs, significantly reducing reliance on cloud resources and enabling privacy-preserving AI applications.
How to implement this in your domain
- 1Download and install the TurboFieldfare Mac application on an M-series Mac.
- 2Experiment with running the Gemma 4 26B model locally for various NLP tasks.
- 3Integrate the OpenAI-compatible local server into existing development workflows for testing.
- 4Contribute to the open-source project by providing feedback or code enhancements.
- 5Explore possibilities for privacy-focused AI applications leveraging on-device inference.
Who benefits
Key takeaways
- TurboFieldfare enables large language models to run on M-series Macs with minimal RAM.
- The engine uses an innovative SSD streaming technique for model experts.
- It offers an OpenAI-compatible local server for development and testing.
- On-device AI reduces cloud dependency and enhances privacy.
Original post by gitpusher42
"Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on…"
View on XOriginally posted by gitpusher42 on X · view source
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