OSS Radar
drumih/turbo-fieldfare
Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook. Why it matters: useful plumbing for shipping LLM-powered features.
GitHub description: Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook
Stars
6.9k
Last 24h
+79est.
Last 7 days
+551est.
Forks
439
Star history (last 1 snapshot)
Only one snapshot so far. The chart fills in as OSS Radar collects daily snapshots.
* / est. Estimated: until we have enough daily snapshots for a repo, its gain is total stars ÷ days since creation. Measured numbers replace estimates automatically.
Maintainer? Add the badge to your README
Shows your live OSS Radar rank and links back here. Updates hourly.
[](https://ossradar.net/repo/drumih/turbo-fieldfare)<a href="https://ossradar.net/repo/drumih/turbo-fieldfare"><img src="https://ossradar.net/badge/drumih/turbo-fieldfare" alt="Trending on OSS Radar"></a>About turbo-fieldfare
drumih/turbo-fieldfare is a Swift project in the LLM tooling category, created 3 months ago. It has 6.9k GitHub stars and 439 forks, and gained +551 stars over the last 7 days (early estimate), ranking #74 of 167 LLM tooling repos tracked by OSS Radar this week.
See more trending LLM tooling repos or the top AI repos this week.
- Language
- Swift
- Category
- LLM tooling
- Created
- Jul 17, 2026
- Last push
- Sep 27, 2026
- apple-silicon
- gemma
- gemma4
- gemma4-26b-a4b
- gpgpu
- llm
- llm-inference
- local-ai
- macos
- metal
- on-device-ai
- on-device-llm
- swift
Similar rising repos
- StayLameBro/backburner+594est.
Your iPhone helps your Mac run a 27B model: faster prompt reading and more context over a USB-C cable. Why it matters: useful plumbing for shipping LLM-powered features.
- jundot/omlx+658est.
LLM inference server with continuous batching & SSD caching for Apple Silicon — managed from the macOS menu bar. Why it matters: useful plumbing for shipping LLM-powered features.
- omlahore/RemoveMacAI+2.3kest.
Debloat macOS: turn off Apple Intelligence, analytics, ads and pop-ups. A native app and CLI, and every change can be undone. Why it matters: useful plumbing for shipping LLM-powered features.
- ollama/ollama+1.1kest.
Get up and running with Kimi, GLM, MiniMax, DeepSeek, gpt-oss, Qwen, Gemma and other models. Why it matters: useful plumbing for shipping LLM-powered features.
- FareedKhan-dev/kimi-k3-in-c+864est.
A 2.78-trillion-parameter Kimi K3 running inference on a single CPU in 8.24 GB of RAM. Portable C99: no BLAS, no framework, no GPU. Why it matters: useful plumbing for shipping LLM-powered features.
- bojieli/ai-infra-book+864est.
《深入理解 AI Infra：量化分析与系统设计》（李博杰 著）开源书稿：从硬件约束和模型架构出发，量化推导 LLM 推理与训练系统设计。含全书正文、PDF、配套计算工具与实验. Why it matters: useful plumbing for shipping LLM-powered features.