Data-driven mixed-precision native TurboQuant checkpoint of Qwen/Qwen3.6-35B-A3B. Same recipe as varjosoft/Qwen3.6-35B-A3B-TQ-apex but additionally skips compression on the small, critical tensors our per-tensor kurtosis scan flagged as extremely heavy-tailed: the MoE router gates (mlp.gate.weight, κ mean 17.5, max 25.6) and the GatedDeltaNet linear-attention projections (in_proj_b, out_proj, linear_fc). Those ~40-200 MB stay at fp16/bf16.
This is PAT-0349 Kurtosis-Aware Automatic Bit-Width Selection applied one step further than -TQ-apex: not just raising bit width for moderate-κ tensors, but leaving the highest-κ tensors uncompressed where the scalar grid fundamentally can't represent them.
Why this variant exists
Per the per-tensor κ profile we ran on Qwen3.6-35B-A3B:
Family
Tensors
Mean κ
Max κ
PAT-0349 bucket
apex2 does
Routed gate_proj / up_proj
82
3.4
5.6
TQ3
TQ3 ✓
Routed down_proj
41
3.4
6.9
TQ3
TQ3 ✓
Attention Q/K/V/O
44
5-8
10-26
TQ4
TQ4 ✓
Shared expert gate_proj
41
9.0
23
mixed TQ4/FP16
TQ4
Shared expert up_proj
41
5.6
12
TQ4
TQ4 ✓
Shared expert down_proj
41
16.3
67
FP16 (all 41)
TQ4 ← under-shot
Router mlp.gate.weight
41
17.5
25.6
FP16
FP16 ✓ (skipped)
GatedDeltaNet in_proj_b / out_proj / linear_fc
~60
8-500
>500
mixed FP16/TQ4
FP16 ✓ (skipped)
Bit-plan bucket distribution (from scripts/kappa-to-bit-plan.py)
Bucket
Tensors
Est. total size (κ-optimal)
TQ3 (κ<4)
159
~12.5 GB
TQ4 (4≤κ<8)
244
~1.4 GB
TQ5 (8≤κ<15) → promoted to FP16 (no kernel yet)
90 tensors, ~0.3 GB at fp16
—
FP16 (κ≥15)
148
~0.6 GB
κ-optimal total
541
~14.4 GB
uniform-TQ3 baseline
—
~13.6 GB
A fully κ-optimal checkpoint would be ~14.4 GB on disk (0.8 GB more than uniform TQ3) while matching bf16 quality on the highest-κ tensors. apex2 captures most of that ceiling cheaply by skipping what's clearly FP16-bucket.
-TQ-apex (the predecessor) forces all 41 router gates through the scalar grid at TQ3, damaging softmax routing precision — the main residual quality gap vs MLX-4bit reference. Skipping them costs ~40 MB on disk (gate tensors are small: hidden × num_experts ≈ 1 MB each × 40 layers). Other skip patterns add a similar amount.
Measured quality and throughput
Benched on M4 Pro 48 GB, gsm8k-200 5-shot CoT @ max_tokens=1024:
Variant
Disk
gsm8k-200
Aggregate tok/s
uniform TQ3
16 GB
85.5 %
35
-TQ-apex (TQ3 + TQ4 mixed)
17 GB
93.0 %
36
this (-TQ-apex2)
18 GB
96.0 %
17.5
mlx-community/*-4bit (reference)
19 GB
94.5 %
79
apex2 beats the MLX-4bit reference on accuracy (96.0 vs 94.5) while being 1 GB smaller on disk. The quality gap vs base model is effectively closed for reasoning tasks of this kind.
How to use
Same loader as -TQ-apex — the fp16-kept tensors automatically pass through mlx_lm's standard weight-load path (they don't carry .tq_packed sidecars, so the TurboQuant loader ignores them):
Needs ≥ 100 GB CPU RAM (full bf16 load during compression). Inference: ~20 GB resident.
Citations
@article{qwen2026qwen36,
title={Qwen3.6-35B-A3B}, author={Qwen Team, Alibaba}, year={2026},
url={https://huggingface.co/Qwen/Qwen3.6-35B-A3B}
}
@inproceedings{malinovskii2025higgs,
title={HIGGS: Pushing the Limits of Large Language Model Quantization via
Hadamard Rotations and MSE-Optimal Grids},
author={Malinovskii, Vladimir and Mazur, Andrei and Ilin, Ivan and Kuznedelev,
Denis and Burlachenko, Konstantin and Yi, Kai and Alistarh, Dan and
Richtarik, Peter},
booktitle={NAACL}, year={2025},
url={https://aclanthology.org/2025.naacl-long.543/}
}
Mixed-precision bit-width selection draws on PAT-0349 (Kurtosis-Aware Automatic Bit-Width Selection) and the MXPLM survey (Mixed-Precision Quantization for Language Models, arXiv, Oct 2025).
