turbo-fieldfare

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.

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 Model 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.

The measured result is a reference point, not a performance ceiling. Prompt length, generated length, page-cache state, and hardware all affect throughput. See community benchmark results from other Macs, or follow the community benchmark guide to add your own.

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:

• An Apple Silicon Mac; the validated target is an 8 GB M2 MacBook Air
• macOS 26 with Metal 4
• Apple Command Line Tools or Xcode with Swift 6.2+ and the macOS 26 SDK 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.

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.

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. Audio and video are not supported.

Images are supported through a vision tower, which installs as a companion pack beside the text model. Install it once and the app, CLI, and server all accept images. Without it they tell you image support is unavailable, and the text runtime is untouched. The image tower requires an M2 or newer Apple Silicon Mac; text-only inference remains available on M1.

System design covers how the tower runs and what it costs on an 8 GB machine.

Clone the repository, then run the app from its root:

swift build -c release
.build/release/TurboFieldfareMac

Build the complete package so the app and its sibling decode service are both available. New installs from this checkout default to scratch/gemma4.gturbo; the app remembers a location you choose.

From the repository root, run:

Scripts/build_app.sh

This creates dist/TurboFieldfare.app, which you can double-click or move to Applications. Add --install to also copy it to ~/Applications. The bundle includes the decode service and resources; model weights stay in their selected folder. The script uses ad-hoc signing, so no Apple developer account is required.

Choose Download Model to install, or Change… to pick a destination. To reuse a model, choose Use Existing Model… and select its .gturbo folder or parent folder. If several models are found, pick one. Keep the optional image pack beside its text model: gemma4.gturbo and gemma4.vision.gturbo.

The installer never materializes the full source checkpoint. It streams the required byte ranges from the pinned Hugging Face revision and repacks them directly into the .gturbo layout as they arrive. This avoids a second full checkpoint on disk and keeps scratch memory bounded.

The first installation transfers about 15 GB through bounded Hugging Face range requests. Network speed and Hugging Face response times vary, so it can take a while. The completed .gturbo installation occupies about 14.3 GB and is accepted only after its manifest and file hashes have been validated. Installation does not load the model into memory.

After installation:

1. Choose Load Model.
2. Enter a prompt in the composer.
3. Choose Generate, or press Command+Return. Use Settings > Send Message With to choose Return or Command-Return.
4. Send another message to continue the conversation, or choose New Chat to start over.
5. Use the stop button or Escape to end generation early.

The status bar shows generation progress, decode speed, and memory use. Use the right pane to configure sampling, context length, expert-cache slots, and runtime options. See Runtime controls for details and defaults.

Use Settings → Text Size to enlarge conversation and message-editor text from 100% to 200%. Changes apply immediately and are remembered across launches.

The app saves chats locally so you can browse and continue them from the sidebar. Every launch opens an empty chat. Only one conversation's model context (KV cache) stays in memory. Browsing other chats keeps that cache intact; continuing another chat replaces it. New Chat starts fresh.

Settings and chats live in ~/Library/Application Support/TurboFieldfare/ (mac-app-settings.json and conversations/). A standalone app also uses this folder for new model downloads by default. Moving the app does not move these files or change a saved model location.

The CLI uses an existing .gturbo installation. For a model installed through the Mac app, pass its location with --model. To install from the command line:

swift run -c release TurboFieldfareRepack \
  --output scratch/gemma4.gturbo \
  --overwrite

Continue a cancelled or interrupted download:

swift run -c release TurboFieldfareRepack \
  --output scratch/gemma4.gturbo \
  --overwrite \
  --resume

Remove saved download state:

swift run -c release TurboFieldfareRepack \
  --discard-partial \
  --output scratch/gemma4.gturbo

The runtime accepts only a completed .gturbo directory with a final manifest.json.

Verify an existing installation without loading the model:

swift run -c release TurboFieldfareRepack \
  --verify-install \
  --input-gturbo scratch/gemma4.gturbo

The companion pack installs beside the text model:

swift run -c release TurboFieldfareRepack \
  --vision-output scratch/gemma4.vision.gturbo \
  --text-model scratch/gemma4.gturbo

The pack adds about 1.1 GB. Verify it with --verify-vision-install, remove an installed one with --remove-vision-install, and drop a cancelled download with --discard-partial --vision-output <dir>. A cancelled transfer can also be continued with --resume. The Mac app installs the same pack from its Image Support section.

swift run -c release TurboFieldfareCLI \
  --model scratch/gemma4.gturbo \
  --chat-prompt "What is in this picture?" \
  --image photo.jpg

--image is repeatable and requires --chat-prompt; it cannot be combined with --prompt or --messages-file. Without the companion pack the run stops and says image support is unavailable.

A multi-turn conversation carries its images inside the messages file instead, as image_file parts in any user message; --image covers the single-turn case only. Either route needs at least 16 expert-cache slots, because an image prompt always prefills chunked.

Put chat messages in a JSON array and pass it with --messages-file:

[
  {"role": "user", "content": "Explain why chunked prefill reduces time to first token while keeping memory bounded."}
]

swift run -c release TurboFieldfareCLI \
  --model scratch/gemma4.gturbo \
  --messages-file messages.json

This formats messages in the same way as the Mac app. The CLI response limit is set with --max-new, which defaults to 1,024 tokens. The Mac app can generate until the selected context window is full.

Common generation options include --max-context, --temperature, --top-k, --top-p, --repetition-penalty, --seed, and repeatable --stop strings. Runtime options include --expert-cache-slots, --expert-cache-policy, --prefill, --prefill-chunk-tokens, and --rdadvise; omitted options use the production defaults. Run the following command for the complete option list:

swift run -c release TurboFieldfareCLI --help

Generated text goes to standard output. Timing statistics go to standard error; add --quiet to suppress that footer in scripts.

Build the server and point it at an installed model:

swift build -c release --product TurboFieldfareServer
.build/release/TurboFieldfareServer \
  --model scratch/gemma4.gturbo

It listens on http://127.0.0.1:8080/v1 and supports Chat Completions, streaming, function tools, and single-prefix prompt reuse. The client must authorize and run every tool call. Keep the server on loopback; it has no remote authentication or TLS.

See Local server for a test request, Python and OpenCode setup, prompt reuse, tool handling, and the supported API subset.

Run the public test suite serially:

Scripts/test.sh

Before starting a model run, close memory-heavy apps and check memory_pressure -Q. If it reports little free memory, postpone the run. Run only one TurboFieldfare app, decode service, CLI, server, test, or other local-model process at a time.

To contribute a comparable performance result, follow the community benchmark guide.

At each transformer layer, Metal computes attention and the router from resident weights. The CPU uses the router's top-8 expert IDs to plan against the layer's 16-slot LFU cache, then fills misses with bounded parallel pread calls into Metal-visible buffers. Metal computes the resident shared-expert branch while those reads run, then combines the shared and routed outputs.

Prompt prefill uses chunks of up to 128 tokens so one fetched expert can serve multiple rows. Generation repeats the routed layer loop one token at a time. The installer applies the same bounded-memory rule: it repacks remote ranges directly into .gturbo without staging a full shard or tensor.

For a video overview of TurboFieldfare, see Better Stack's Local AI On Apple Silicon uses 7X Less RAM.

For a visual introduction to the model architecture, see Maarten Grootendorst's A Visual Guide to Gemma 4.

System design explains the .gturbo layout, memory ownership, prefill, router handoff, cb1/io/cb2 phases, Metal kernels, and correctness invariants.

TurboFieldfare currently includes:

• Remote streaming repack into the .gturbo model format
• Instruction-tuned Gemma 4 26B-A4B with verified chat formatting
• 4-bit MLX affine embedding, attention, shared-expert, and routed-expert weights, with an 8-bit router
• Custom Metal kernels for quantized GEMV, attention, MoE, normalization, RoPE, sampling, and production fusions
• SSD-backed routed-expert streaming with a bounded expert cache
• Chunked prefill for one-shot prompts and new conversational turns, followed by token-by-token generation
• FP16 KV storage with bounded circular storage for 25 sliding-window layers and linear storage for 5 full-attention layers
• Exact split-K/V decode attention with distinct normalized K and V paths
• A Swift library, streaming installer, command-line interface, loopback OpenAI-compatible server, and native SwiftUI/AppKit Mac app with a sibling local decode service
• Optional image input from a separately installed companion pack: the vision tower runs on bounded scratch, image rows attend in both directions inside the sliding-window layers, and routed experts are released while the tower runs

Current scope is text input from the pinned Gemma 4 26B-A4B instruction checkpoint on Apple Silicon Macs with at least 8 GB of RAM, plus image input on M2 or newer Macs. Audio and video are out of scope.

• Build iPhone and iPad apps, then measure inference speed and memory use on mobile hardware.
• Benchmark more Apple Silicon Macs, especially the base 16 GB M4 Mac mini and other 8 GB models.

The experiments that shaped TurboFieldfare explain the largest wins, the plausible ideas that failed, and the early results that reversed under stronger validation. The detailed experiment record keeps all 103 audited entries as optional evidence.

Useful entry points:

• Local OpenAI-compatible server
• System design
• Benchmarks
• The experiments that shaped TurboFieldfare
• Experiment inventory and summaries
• Implementation references

TurboFieldfare's source and documentation are licensed under the Apache License 2.0.

Model weights are not included. The installer downloads them separately from the pinned Hugging Face checkpoint, and the weights remain governed by their source terms. See THIRD_PARTY_NOTICES.md for the model and Swift package license review.

TurboFieldfare is an independent research project. It is not affiliated with, sponsored by, or endorsed by Google.

Thanks for checking out this project!

My name is Andrey Mikhaylov. You can find me on LinkedIn. I am the author of TurboFieldfare and an iOS and Metal engineer. Most of my work is with images, video, and on-device AI.

I dedicate this project to my wife, Sasha, the most supportive person I know. She stands by me even through the hardest times. She loves wildlife, goes birdwatching, and volunteers with our local birding community. Because of her, I have also grown closer to birds and nature.

TurboFieldfare is named after the fieldfare, a member of the thrush family and my favourite bird. It is not the most noticeable or brightly coloured bird, but it definitely has a character and unique features of its own. I think the same is true of this project: it may not be the most practical, but I built it with my favourite tools, especially Metal, in my favourite field, on-device ML inference. It definitely has its own character and unique features.

Next time you are outside, touch the grass and listen to the birds. Sometimes it is the most beautiful thing you can do. And if you can, support your local wildlife community. They do important work.

Thank you!