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A 4-bit mixed-precision MLX quant produced by mlx-optiq, built on Google's quantization-aware-trained (QAT) Gemma-4 base. This is the flagship of the family: the 31B dense Gemma-4, and the strongest OptiQ quant on the Capability Score. OptiQ's sensitivity-guided per-layer bit allocation is applied on top of weights already trained to survive low-bit quantization, and it still beats a uniform 4-bit quant of the same QAT base by +1.65 Capability Score points.

This is a quant of google/gemma-4-31B-it-qat-q4_0-unquantized. Per-layer bit-widths come from a KL-divergence sensitivity pass on a six-domain calibration mix (prose, reasoning, code, agent, tool-call, constraint-bearing instructions). Sensitive layers go to 8-bit, robust ones stay at 4-bit.

Quantization details

Property	Value
Base	google/gemma-4-31B-it-qat-q4_0-unquantized (QAT, dense)
Predominant precision	4-bit
Components at 8-bit (sensitive)	186
Components at 4-bit (robust)	224
Total quantized components	410
Achieved bits-per-weight	5.20
Group size	64
Reference for sensitivity	uniform 4-bit (streamed)
Calibration mix	six-domain mix
Vision	bf16 sidecar (`optiq_vision.safetensors`), image+text via optiq
Speculative drafter	`google/gemma-4-31B-it-qat-q4_0-unquantized-assistant` via `optiq serve --drafter`

Capability Score

Six-metric mean (MMLU, GSM8K, IFEval, BFCL, HumanEval, HashHop), scored against a uniform 4-bit quant of the same QAT base. That comparison isolates what the mixed-precision allocation adds, holding the base fixed.

Benchmark	This model (OptiQ, QAT base)	Uniform-4 (QAT base)	Delta
MMLU (5-shot, 1000)	72.7%	72.4%	+0.3
GSM8K (1000)	96.3%	96.6%	-0.3
IFEval (full, strict)	77.8%	77.4%	+0.4
BFCL-V3 simple (200)	93.0%	93.0%	+0.0
HumanEval (pass@1, 164)	93.3%	92.7%	+0.6
HashHop (long-context)	59.0%	50.0%	+9.0
Capability Score (mean)	82.01	80.36	+1.65

OptiQ adds +1.65 points over uniform 4-bit on this QAT base, the largest margin in the Gemma-4 family alongside the small QAT models (E2B +2.09, E4B +1.19, 12B +1.37). The gain concentrates in long-context retrieval (HashHop +9.0): the per-layer allocation puts 8-bit on the attention and projection layers that carry the retrieval signal, which the larger model leans on most. The mixed quant is 5.20 bits-per-weight (about 20.8 GB on disk) versus 4.0 bits-per-weight (about 16.1 GB) for uniform 4-bit, with the extra budget spent on the layers that need it.

Usage

This is a Gemma-4 (model_type: gemma4, gemma4_text), so it needs mlx-lm from main and import optiq (the Gemma-4 text tower is not in the 0.31.3 PyPI release; the main build also reports 0.31.3, so install from git, not a version pin):

pip install -U mlx-optiq "mlx-lm @ git+https://github.com/ml-explore/mlx-lm.git"

python

import optiq  # registers the OptiQ model paths
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/gemma-4-31B-it-qat-OptiQ-4bit")
print(generate(model, tokenizer, "Explain mixed-precision quantization.", max_tokens=256))

Image+text input and the speculative drafter run through mlx-optiq:

bash

pip install mlx-optiq
optiq serve --model mlx-community/gemma-4-31B-it-qat-OptiQ-4bit \
            --drafter google/gemma-4-31B-it-qat-q4_0-unquantized-assistant

The language and image+text paths both run through optiq. The bf16 vision tower rides in optiq_vision.safetensors, which mlx-lm ignores (it globs model*.safetensors), so both paths work from one artifact.