diffusiongemma-26B-A4B-it-mini-g32 — for 16 GB Macs

9.79 GB TurboQuant build of google/diffusiongemma-26B-A4B-it, sized to run on a 16 GB Apple Silicon Mac (Mac mini class), produced with TurboQuant-MLX. Sibling of the higher-fidelity tq3-g32 build (13.8 GB — needs ~18 GB peak, OOMs on 16 GB machines).

Documentation: manjunathshiva.github.io/turboquant-mlx: the DiffusionGemma page, getting started, choosing a model for your Mac's memory, guides for serving an OpenAI-compatible API, local coding agents, vision models and memory and macOS settings.

Precision layout

Why the protection: raw 2-bit experts break arithmetic on this model (17×23 → "3"). Keeping the first/last three layers' experts at 3-bit restores it for +0.2 GB — measured: 17×23 = 391 ✓, multi-step chains correct, in-context recall exact, prose with only minor artifacts.

Will it fit my Mac?

Don't guess, and don't download 10.5 GB to find out — ask first (needs turboquant-mlx-full >= 0.15.1):

turboquant-plan --model manjunathshiva/diffusiongemma-26B-A4B-it-mini-g32

It reads only this repo's safetensors headers over the network (a couple of hundred KB, a few seconds — the weights are never fetched), then projects the peak against your machine and prints the flags to use:

Model
  weights (exact)      10.5 GB
  KV cache             65.0 KB/token  (hybrid: 5/30 full-attention, 25 sliding (window 1024))

Verdict: <resident | needs a wired-limit raise | streaming>
Recommended: <the exact flags for your Mac>

Planning for a machine you're not sitting at — deciding whether a 16 GB mini or a 64 GB Mac can take this — is what --wired-gb / --ram-gb are for:

turboquant-plan --model manjunathshiva/diffusiongemma-26B-A4B-it-mini-g32 \
    --wired-gb 10.5 --ram-gb 16 --context 8192

turboquant-doctor runs the same projection plus a readiness check; both take --json. The projection is calibrated against real measurements on a 16 GB Mac mini rather than estimated from theory — on the 9.4 GB ternary 35B it predicts a 10.44 GB peak where that machine measures 10.42.

Measured (Apple Silicon)

Verified on a 16 GB Mac mini (after the wired-limit bump below): 96 tokens at ~1.5 tok/s, 12.9 GB peak, coherent output. Speed knob: --max-denoising-steps 24 is ~2x faster at a mild quality cost.

On a 16 GB Mac the default Metal working-set limit is ~12.1 GB and the mini build peaks just above it (~12.4 GB at --max-tokens 120), so raise the Metal wired limit first (verified working on a 16 GB Mac mini; resets on reboot):

sudo sysctl iogpu.wired_limit_mb=13824

Alternatively keep the canvas (and its activations) smaller with --max-tokens 96.

Requirements

pip install "turboquant-mlx-full[vlm]>=0.8.0"

Quick Start

python -m turboquant_mlx.generate_vlm \
    --model manjunathshiva/diffusiongemma-26B-A4B-it-mini-g32 \
    --prompt "Write a short paragraph about the ocean." \
    --max-tokens 120 --temp 0.0

Optional speed knob: --max-denoising-steps 24 (~2x faster, mild quality cost — quantized diffusion needs more denoising iterations than bf16).

Reproducing the conversion

python -m turboquant_mlx.convert_vlm \
    --hf-path google/diffusiongemma-26B-A4B-it \
    --mlx-path ./diffusiongemma-26B-A4B-it-mini-g32 \
    --bits 2 --attn-bits 3 -g 32 \
    --protect-expert-layers 0,1,2,27,28,29 --protect-bits 3 \
    --quantize-extras

License

Apache 2.0, subject to the Gemma license terms (same as the base model). Quantization tooling: TurboQuant-MLX.

Copyright 2026 Manjunath Janardhan.

Citation

@article{zandieh2025turboquant,
  title={TurboQuant: Online Vector Quantization with Near-optimal Distortion Rate},
  author={Zandieh, Amir and Daliri, Majid and Hadian, Majid and Mirrokni, Vahab},
  year={2025},
  eprint={2504.19874},
  archivePrefix={arXiv},
  primaryClass={cs.LG},
  url={https://arxiv.org/abs/2504.19874}
}