Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac
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 your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac launched on July 29, 2026 with 919 votes, #8 of 24,623 launches that month and more than 99% of that year's launches.
In plain words
TurboFieldfare is an inference engine built in Swift and Metal that enables M-series Macs to run the Gemma 4 26B model using approximately 2 GB of RAM. It uses a specialized technique that keeps essential model components in memory while streaming only needed weights from the SSD during inference. The engine is designed for users who want to run large language models locally on their Mac hardware, even with limited RAM, by optimizing memory usage and leveraging the GPU alongside efficient disk access patterns.
written from the facts on this page · September 2026
From the sources
Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook - drumih/turbo-fieldfare
Gemma 4 26B-A4B inference in about 2 GB of RAM A custom Swift + Metal runtime for any Apple Silicon Mac, even the 8 GBfrom github.com
In the maker’s words, at launch
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 your Mac or iPhone. So I wanted to…
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