Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac

TurboFieldfare Gemma 4 26B-A4B inference in about 2 GB of RAM A custom Swift + Metal runtime for any Apple Silicon Mac, even the 8 GB ones. Quick start · Local server · Benchmarks · Contribute results · How it works · Experiments · References Memory got expensive. So I gave a 26-billion-parameter model a ~2 GB budget. TurboFieldfare runs the instruction-tuned Gemma 4 26B-A4B without loading the entire 14.3 GB model into memory. It keeps the shared 1.35 GB core and FP16 KV cache in memory, then streams only the experts needed for each token from SSD. This is what lets the model run on Macs with 8 GB of RAM. The runtime, streaming installer, CLI, and native Mac app are written in Swift and Metal. TurboFieldfare is model-specific rather than a wrapper around MLX or llama.cpp. The curated experiment record summarizes 103 measured results across kernels, caching, I/O, prefill, and decode. Try it git clone https://github.com/drumih/turbo-fieldfare.git cd turbo-fieldfare swift build -c release .build/release/TurboFieldfareMac On the first run, Swift Package Manager downloads and builds the Swift packages required by the tokenizer. The complete release build includes the foreground Mac app and its sibling decode-service executable. When the app opens, choose Download and let TurboFieldfare fetch and repack the pinned model (about 15 GB). Once it is ready, choose Load Model, type your prompt, and press Generate. At a glance Metric Value Model Gemma 4 26B-A4B IT, 26B total parameters, about 3.88B active per token Weights MLX affine 4-bit, group 64; 8-bit router; 4-bit shared and routed experts Memory ~2 GB of weights and 4K KV cache Storage About 14.3 GB for the installed text-only model Hardware Apple Silicon Mac; 8 GB of RAM Platform macOS 26, Metal 4, Swift 6.2 M2 measured decode 5.1-6.3 tok/s on an 8 GB M2 MacBook Air M5 measured decode 31-35 tok/s on a 24 GB M5 Pro The measured result is a reference point, not a performance ceiling. Prompt length, generated length, page-cache state, and hardware all affect throughput. To help measure another Apple Silicon Mac, follow the community benchmark guide. Using TurboFieldfare TurboFieldfare provides a native Mac app, a command-line interface, and an experimental loopback OpenAI-compatible server. They use the same .gturbo model directory, but only one model-owning product should run at a time. The Swift package exposes six products: Product Purpose TurboFieldfare Swift library containing the runtime and Metal kernels TurboFieldfareMac Native Mac app for installation and generation TurboFieldfareDecodeService One-shot local model and Metal owner used by the Mac app TurboFieldfareCLI Command-line instruction chat and raw completion TurboFieldfareServer Loopback OpenAI-compatible Chat Completions server TurboFieldfareRepack Streaming model installer and install verifier Requirements An Apple Silicon Mac; the validated target is an 8 GB M2 MacBook Air macOS 26 with Metal 4 Xcode 26 and Swift 6.2 or newer Enough free storage for the ~14.3 GB model installation An internet connection for the first model install The package is arm64-only. Older macOS and Metal versions are not supported. Prompting the model The Mac app treats what you type as an instruction and handles Gemma's chat formatting automatically. Just describe the task and include any context the model needs. Generation defaults to temperature 0.2, Top-K 64, and Top-P 0.95. Set temperature to 0 for deterministic greedy output. The model can still repeat itself or give incorrect answers, so check important results. TurboFieldfare is text-only. The app and CLI support user and model messages plus optional system guidance; they do not expose or execute tools. The loopback server accepts function-tool declarations and returns model-produced tool calls for the client to authorize and execute. Images, audio, and video are not supported. Mac app Clone the repos