	Original (BF16)	UD-Q3_K_XL (this model)
Size	~62 GB	21 GB
Format	SafeTensors	SafeTensors
Precision	BF16 uniform	3-bit affine + mixed bits + BF16
FFN group size	—	64
Biases	—	yes

All Variants

Repo	Bit budget	Size	Decode (tok/s)
Brooooooklyn/Gemma-4-31B-IT-UD-Q2_K_XL-mlx	2-bit base	18 GB	12.1
Brooooooklyn/Gemma-4-31B-IT-UD-Q3_K_XL-mlx (this model)	3-bit base	21 GB	10.4
Brooooooklyn/Gemma-4-31B-IT-UD-Q4_K_XL-mlx	4-bit base	24 GB	9.2
Brooooooklyn/Gemma-4-31B-IT-UD-MXFP4_K_XL-mlx	mxfp4	24 GB	10.6
Brooooooklyn/Gemma-4-31B-IT-UD-NVFP4_K_XL-mlx	nvfp4	24 GB	9.9
Brooooooklyn/Gemma-4-31B-IT-UD-Q5_K_XL-mlx	5-bit base	28 GB	6.8
Brooooooklyn/Gemma-4-31B-IT-UD-Q6_K_XL-mlx	6-bit base	31 GB	7.2
Brooooooklyn/Gemma-4-31B-IT-UD-MXFP8_K_XL-mlx	mxfp8	34 GB	7.8
Brooooooklyn/Gemma-4-31B-IT-UD-Q8_K_XL-mlx	8-bit base	35 GB	7.2

Benchmarked on Apple M3 Max 128GB via examples/lm.ts (best decode tok/s across turns 2–4, steady-state, capitals chat with reasoningEffort: 'low').

Performance

Steady-state decode: 10.4 tok/s on Apple M3 Max 128GB (best of turns 2–4, examples/lm.ts capitals chat with reasoningEffort: 'low'). Decode is memory-bandwidth bound on Apple Silicon — fewer bytes per token directly translates to higher throughput. Gemma-4-31B is fully dense (all 31B parameters active per token), so each decoded token must stream the entire quantized weight footprint from unified memory.

Per-Tensor Bit Assignments (N=3)

Weight	Mode	Bits	Group	Rationale
`embed_tokens`	5-bit affine	5	64	Tied with lm_head (Gemma4 shares weights); affine-only loader
`self_attn.q_proj`	5-bit affine	5	64	AWQ-corrected via input_layernorm
`self_attn.k_proj`	5-bit affine	5	64	AWQ-corrected via input_layernorm
`self_attn.v_proj`	5-bit affine	5	64	AWQ-corrected via input_layernorm
`mlp.gate_proj`	3-bit affine	3	64	Dense MLP (top-level default) (top-level default)
`mlp.up_proj`	3-bit affine	3	64	Dense MLP (top-level default) (top-level default)
`mlp.down_proj`	4-bit affine	4	64	Dense MLP; "slightly more sensitive" (unsloth `base+1`)
`self_attn.o_proj`	bf16	—	—	NOT AWQ-correctable; kept full-precision

Quantization Strategy

Built on Unsloth Dynamic 2.0 per-tensor KLD analysis. At --q-bits 3 the unsloth recipe assigns the base bits to MLP gate/up projections (the bulk of the parameter budget), base+1 to down_proj (slightly more sensitive), base+2 (snapped to a valid bit width) + AWQ pre-scaling to attention q/k/v projections, base+2 to embed_tokens / lm_head (tied weights), and keeps self_attn.o_proj as bf16 (AWQ-uncorrectable — its inputs come from the attention compute, not from a norm layer).

imatrix AWQ pre-scaling amplifies important weight channels and fuses inverse scales into preceding layer norms (zero inference overhead).

Architecture

Parameter	Value
Total parameters	~31B (fully dense — all parameters active per token)
Hidden size	5,376
Layers	60 (sliding-window attention)
Attention heads	32 (16 KV heads, GQA 2:1)
Head dimension	256
MLP intermediate size	21,504
Vocab size	262,144
Max context	131,072 tokens
Vision	yes (Gemma4ForConditionalGeneration)

Usage

typescript

import { loadSession } from '@mlx-node/lm';

const session = await loadSession('./Gemma-4-31B-IT-UD-Q3_K_XL-mlx');

for await (const event of session.sendStream('Explain the sliding-window attention design in Gemma-4.', {
  config: { maxNewTokens: 2048, temperature: 0.6, reasoningEffort: 'low' },
})) {
  if (!event.done) process.stdout.write(event.text);
}

How It Was Made

bash

mlx convert \
  -i gemma-4-31b-it \
  -o Gemma-4-31B-IT-UD-Q3_K_XL-mlx \
  -q --q-bits 3 --q-recipe unsloth \
  --imatrix-path imatrix_unsloth.gguf

Acknowledgments

Unsloth — Quantization strategy based on their per-layer KLD benchmarks and Dynamic 2.0 methodology
Google DeepMind — For the Gemma-4 model family
Apple MLX — For the Metal-accelerated ML framework

License

Gemma Terms of Use (inherited from base model).

Brooooooklyn/Gemma-4-31B-IT-UD-Q3_K_XL-mlx

Gemma-4-31B-IT — UD-Q3_K_XL (mlx-node)