**TurboFieldfare** runs Google's instruction-tuned **Gemma 4 26B-A4B** in about **2 GB of RAM** on any Apple Silicon Mac, including 8 GB machines, by never loading the full model at all. The repository has collected **6,162 stars and 371 forks** since its **17 July 2026** creation and was last pushed on **18 August 2026**. It is an independent research project by **Andrey Mikhaylov**, an iOS and Metal engineer, and the README states plainly that it is not affiliated with, sponsored by, or endorsed by Google — the model is Google's, the runtime is not. The mechanism is expert streaming rather than compression alone. The installed model occupies about **14.3 GB on disk**, of which TurboFieldfare keeps only the **shared 1.35 GB core plus an FP16 KV cache** resident, then streams from SSD only the experts a given token actually routes to. Gemma 4 26B-A4B has **26B total parameters with roughly 3.88B active per token**, which is what makes the trade viable. Weights are **MLX affine 4-bit at group 64**, with an 8-bit router and 4-bit shared and routed experts. Per layer, Metal computes attention and the router from resident weights; the CPU takes the router's **top-8 expert IDs** and plans against that layer's **16-slot LFU cache**, filling misses with bounded parallel `pread` calls straight into Metal-visible buffers. While those reads are in flight Metal computes the resident shared-expert branch, then combines the shared and routed outputs. Prompt prefill runs in **chunks of up to 128 tokens** so one fetched expert can serve multiple rows. KV storage is FP16, using **bounded circular storage for 25 sliding-window layers and linear storage for 5 full-attention layers**. The published numbers are measured rather than projected, and the README frames them as reference points rather than ceilings: **5.1–6.3 tok/s decode on an 8 GB M2 MacBook Air** and **31–35 tok/s on a 24 GB M5 Pro**, with a community benchmark guide for contributing results from other machines. Behind that sits an unusually disciplined evidence trail — a curated experiment record of **103 measured results** across kernels, caching, I/O, prefill and decode, including the plausible ideas that failed and the early results that reversed under stronger validation. The Swift package exposes six products: the runtime library, a native SwiftUI/AppKit **Mac app**, a one-shot decode service, a **CLI**, a loopback **OpenAI-compatible Chat Completions server**, and a streaming installer. The installer is itself part of the memory story — it never materializes the full source checkpoint, instead streaming byte ranges from a pinned Hugging Face revision and repacking them directly into the `.gturbo` layout as they arrive, accepting the result only after manifest and file hashes validate. The scope limits are strict and clearly stated. It is **model-specific rather than a wrapper around MLX or llama.cpp**, so it runs the pinned Gemma 4 26B-A4B checkpoint and nothing else. It requires **macOS 26 with Metal 4, Swift 6.2 and Xcode 26**, is arm64-only, and does not support older macOS or Metal versions. It is **text-only** — no images, audio or video — and the app and CLI neither expose nor execute tools, though the loopback server will return model-produced tool calls for a client to authorize. That server has **no remote authentication or TLS** and is meant to stay on loopback. Licensed **Apache-2.0**, with model weights downloaded separately and governed by their own terms.