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 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 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.
| 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 | |
31-35 tok/son a 24 GB M5 ProThe 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.
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 |
- 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.
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.
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. When launched from this checkout, the app stores the model in
scratch/gemma4.gturbo
.
On first launch, the app checks the available storage and shows the download and installed sizes. Choose Download to begin.
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:
- Choose Load Model. - Enter a prompt in the composer.
- Choose
Generate, or press
Command+Return. - Use the stop button orEscapeto 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.
The CLI uses an existing .gturbo
installation. If you installed the model
through the Mac app, it is already available at scratch/gemma4.gturbo
. Otherwise, install it 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 the model:
swift run -c release TurboFieldfareRepack \
--verify-install \
--input-gturbo scratch/gemma4.gturbo
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.
--prompt
is available for raw completion and reproducible comparisons. It
passes the text directly to the model without chat formatting. Use
--messages-file
for instruction-response conversations.
swift run -c release TurboFieldfareCLI \
--model scratch/gemma4.gturbo \
--prompt "The capital of France is" \
--max-new 64 \
--temperature 0
This example deliberately requests a short greedy completion.
Common generation options include --max-context
, --temperature
, --top-k
,
--top-p
, --repetition-penalty
, --seed
, and repeatable --stop
strings. The public CLI uses production runtime 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 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 text-only 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 single-prompt prefill and 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 one-shot local decode service
Current scope is text-only inference from the pinned Gemma 4 26B-A4B instruction checkpoint on Apple Silicon Macs with at least 8 GB of RAM.
- 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 serverSystem designBenchmarksThe experiments that shaped TurboFieldfareExperiment inventory and summariesImplementation 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!