Model Releases
Turbo-fieldfare: Open-source engine running Gemma 4 26B in 2 GB RAM on Apple Silicon
Its a custom Swift/Metal inference engine that runs Gemma 4 26B-A4B-IT on M-series Macs with very low RAM. It uses ~2GB instead of ~14 GB. The result is reportedly 5–6 tok/s on an 8 GB M2 MacBook Air
Its a custom Swift/Metal inference engine that runs Gemma 4 26B-A4B-IT on M-series Macs with very low RAM. It uses ~2GB instead of ~14 GB. The result is reportedly 5–6 tok/s on an 8 GB M2 MacBook Air and 31–35 tok/s on an M5 MacBook Pro. It also includes an OpenAI-compatible local server with streaming and tool-call support. submitted by /u/minefew [link] [comments]
Related
- Apple M5 isn't making full use of its matmul cores yet
- DKV: Open-source KV-cache compression framework for local LLM inference (CLI + technical report)
- Open-TQ-Metal: Fused Compressed-Domain Attention for Long-Context LLM Inference on Apple Silicon
- Mobile Offline LLMs: What do you use them for?
Source: r/LocalLLaMA | 2026-07-30