Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac
- ID
- 9450
- Status
- summarized
- Published
- 29 Jul 2026, 11:05 PM
- Fetched
- 31 Jul 2026, 11:59 PM
- Provider
- Hacker News
- Category
- dev-community
- Original URL
- https://github.com/drumih/turbo-fieldfare
- Source URL
- https://hnrss.org/best
Summary
- Score
- 7.5
- Created
- 01 Aug 2026, 12:01 AM
- Tags
- Audience
- developersai_ml_learnersvibe_coders
What happened
TurboFieldfare is an open-source Swift + Metal inference engine that runs Gemma 4 26B-A4B in ~2 GB of RAM on any Apple Silicon Mac, including 8 GB models. It keeps only the 1.35 GB shared core and FP16 KV cache in memory, streaming individual experts from SSD per token instead of loading the full 14.3 GB model. The project includes 103 measured benchmark results across kernels, caching, I/O, prefill, and decode.
Why it matters
If you develop on an M-series Mac with limited RAM, this lets you run a 26B-parameter model locally without cloud API costs or memory upgrades. The expert-streaming-from-SSD approach is worth studying if you build on-device inference tooling, as it demonstrates a concrete memory budgeting strategy that trades I/O bandwidth for RAM footprint.
Discussion angle
Compare the expert-streaming-from-SSD tradeoff against quantized models or cloud APIs for a Malaysian developer on an 8 GB M1 MacBook Air — when does the SSD I/O latency make this impractical versus paying per-token API costs?