👋 I built this line solo - the calibration, the per-tensor allocations, and the eval harness behind every number below. I'm looking for internships in AI agent orchestration and model inference. If this work looks relevant to your team: linkedin.com/in/theaaryankapoor

State-of-the-art GGUF quantizations for Qwen/Qwen3.5-9B. Eight builds (8.95 B params, 32 layers - 24 Gated-DeltaNet + 8 full-attention, GQA 4:1), each with a custom per-tensor bit allocation derived for its size point - plus the stock BF16 vision projector.

Benchmarked head-to-head against every published GGUF of this model within ±3 % of any file below - the Unsloth, bartowski, lmstudio-community, mradermacher, byteshape and AtomicChat lines - every file scored on the same rig against the same BF16 reference: 31 wins, 3 statistical ties, 0 losses across 34 paired comparisons on two held-out evaluation sets. The three ties are the near-lossless Q6/Q8 tiers, where the intervals span zero. Recipe-identical clones (the stock Q8_0 uploads across four publishers - header-verified tensor-for-tensor identical recipes - and static k-quants whose same-size imatrix twin is scored) are cited by class rather than re-scored.

One line per publisher. Log y; the AK line sits below every published line at every size point.

Every comparison with its 95 % interval - blue clears zero, grey is a statistical tie. The full numbers are in the table below.

Comparison set: the widest-distribution GGUF lines for this model as published 2026-08-14; the Method section has what is needed to reproduce any number here.

File naming. Every quant in this line carries the AK- prefix: these are custom per-tensor allocations, not llama.cpp's stock recipes, so AK-Q4_K_M and a stock Q4_K_M are different files. mmproj keeps its upstream name.

Tensor-set class. These are quants of the standard 32-block model - the same class as Unsloth's main repo. bartowski's repo folds in the optional MTP speculative head (~259 MB at Q8_0); in every bartowski comparison below their size is text-tower bytes with the MTP block subtracted, so neither side is charged for weights the other doesn't carry.

Which file do I want?

file	size	bpw	mean KLD ↓	top-1 ↑	tg128 t/s (4090)	vs closest rival
`AK-Q8_X`	9.50 GB	8.477	0.002265	98.46 %	91	-17 % vs unsloth Q8_0 (statistical tie)
`AK-Q6_K`	7.46 GB	6.651	0.004698	97.48 %	115	+1 % vs unsloth Q6_K (statistical tie)
`AK-Q5_K_XL`	6.69 GB	5.966	0.007348	96.54 %	122	-59 % KLD vs bartowski Q4_K_L (text-adj)
`AK-Q4_K_XL`	5.94 GB	5.297	0.014039	95.25 %	137	-43 % KLD vs bartowski Q4_1 (text-adj)
`AK-Q4_K_M`	5.67 GB	5.053	0.016320	94.58 %	142	-49 % KLD vs unsloth Q4_K_M
`AK-Q3_K_XL`	5.04 GB	4.491	0.025784	92.96 %	156	-45 % KLD vs unsloth UD-Q3_K_XL
`AK-IQ3_XL`	4.00 GB	3.561	0.083294	87.45 %	173	-25 % KLD vs unsloth UD-IQ3_XXS
`AK-IQ2_M`	3.65 GB	3.245	0.121372	84.94 %	182	-36 % KLD vs unsloth UD-IQ2_M
`mmproj` BF16	0.92 GB	-	-	vision encoder	-	stock, unquantized

AK-Q4_K_XL is the strongest file in the crowded 6 GB class - it beats the most-downloaded quant of this model (Unsloth's UD-Q4_K_XL) by 23 % on mean KLD while being smaller, and the margin holds on all six evaluation domains and at 32k context. At the small end the gap widens: AK-IQ2_M beats UD-IQ2_M by 36 % on KLD at identical bytes, and by ~10 points of HumanEval+ pass@1.

bash

llama-server -m Qwen3.5-9B-AK-Q4_K_XL.gguf \
             --mmproj mmproj-BF16.gguf -c 8192 -ngl 99 --jinja

Usage notes (the three things everyone hits): reasoning is off by default on Qwen3.5 small models - enable with --reasoning on (older builds: --chat-template-kwargs '{"enable_thinking":true}'). Set sampling explicitly (upstream ships no generation_config): thinking/general temp 1.0, top_p 0.95, top_k 20, presence_penalty 1.5; precise coding temp 0.6, top_p 0.95, top_k 20. Use --jinja - never --chat-template qwen2. Needs a llama.cpp with qwen35 support (LM Studio ≥ 0.4.6, runtime ≥ v2.5.1); ollama does not run this architecture yet. Vision needs the mmproj file loaded alongside.

Full measurement table

publisher	file	bytes	bpw	PPL ratio	mean KLD	p99.9 KLD	top-1	Δ vs closest rival
mradermacher	i1-IQ2_M	3,412,436,480	3.036	1.189873	0.236371	4.9514	78.316 %
mradermacher	i1-Q2_K	3,638,519,296	3.238	1.176300	0.239046	5.5994	78.964 %
AaryanK	AK-IQ2_M	3,646,645,440	3.245	1.083664	0.121372	3.5321	84.941 %	-35.8 % [-38.3, -33.4]
Unsloth	UD-IQ2_M	3,649,365,216	3.247	1.146085	0.187737	4.4804	81.061 %
mradermacher	i1-IQ3_XXS	3,793,462,784	3.376	1.082924	0.127881	3.7319	84.330 %
AaryanK	AK-IQ3_XL	4,000,638,144	3.561	1.070108	0.083294	2.5711	87.454 %	-25.4 % [-28.3, -22.8]
Unsloth	UD-IQ3_XXS	4,016,235,744	3.575	1.082550	0.111337	3.1944	85.525 %
byteshape	IQ4_XS-3.60bpw	4,043,231,072	3.599	1.074905	0.114348	2.6627	83.221 %
bartowski	Q2_K†	4,064,274,464	3.618	1.151462	0.175147	4.3790	81.681 %
Unsloth	UD-Q2_K_XL	4,121,781,472	3.670	1.152170	0.176523	4.9628	82.133 %
bartowski	IQ3_XXS†	4,276,021,280	3.807	1.075991	0.102410	2.9732	86.256 %
byteshape	IQ4_XS-4.43bpw	4,967,280,480	4.425	1.009367	0.039375	1.4547	91.381 %
AaryanK	AK-Q3_K_XL	5,040,579,776	4.491	1.018301	0.025784	0.8949	92.959 %	-45.2 % [-50.1, -40.8]
Unsloth	UD-Q3_K_XL	5,053,834,464	4.502	1.019296	0.046866	1.4327	90.958 %
bartowski	Q2_K_L†	5,057,554,464	4.506	1.133528	0.162587	4.0102	82.151 %
mradermacher	i1-IQ4_XS	5,070,611,968	4.517	1.011588	0.029310	0.9714	92.796 %
bartowski	Q3_K_L†	5,111,031,840	4.553	1.025302	0.064598	1.9615	89.162 %
byteshape	Q5_K_S-4.60bpw	5,155,948,384	4.594	1.011517	0.034455	1.0603	91.283 %
Unsloth	IQ4_XS	5,168,653,536	4.605	1.011820	0.039138	1.4409	92.072 %
byteshape	IQ4_XS-4.98bpw	5,581,682,528	4.974	1.010392	0.022439	0.5501	92.754 %
bartowski	Q4_K_S†	5,598,177,312	4.989	1.021660	0.025002	0.7513	93.541 %
lmstudio	Q4_K_M (stock)	5,627,044,256	5.015	1.004992	0.050646	1.6331	90.846 %
mradermacher	i1-Q4_K_M	5,627,045,376	5.015	1.013395	0.033515	1.1563	92.876 %
AtomicChat	Q4_K_M	5,629,109,312	5.016	1.010184	0.033642	1.3279	92.710 %
AaryanK	AK-Q4_K_M	5,669,987,520	5.053	1.010027	0.016320	0.5555	94.582 %	-49.4 % [-57.4, -42.1]
Unsloth	Q4_K_M	5,680,522,464	5.062	1.008938	0.032684	1.1697	92.940 %
byteshape	Q5_K_S-5.10bpw	5,721,438,048	5.099	1.010277	0.021717	0.5636	93.118 %
bartowski	Q4_K_M†	5,910,784,032	5.268	1.016989	0.019750	0.6731	94.189 %
AaryanK	AK-Q4_K_XL	5,943,485,632	5.297	1.008522	0.014039	0.4658	95.254 %	-42.8 % [-49.9, -36.5]
bartowski	Q4_1†	5,944,862,752	5.299	1.012993	0.023863	0.8053	93.587 %
Unsloth	UD-Q4_K_XL	5,966,095,584	5.318	1.013510	0.018934	0.6819	94.594 %
bartowski	Q3_K_XL†	6,001,010,720	5.349	1.021190	0.061208	1.7809	89.382 %
mradermacher	i1-Q5_K_M	6,522,004,992	5.814	0.996076	0.025132	0.8515	94.286 %
Unsloth	Q5_K_M	6,577,841,376	5.864	0.997530	0.010492	0.3184	96.310 %
bartowski	Q4_K_L†	6,665,676,832	5.943	1.006643	0.017368	0.4954	94.475 %
AaryanK	AK-Q5_K_XL	6,691,382,464	5.966	1.006169	0.007348	0.2767	96.544 %	-59.0 % [-66.9, -52.0]
Unsloth	UD-Q5_K_XL	6,743,680,224	6.012	1.002307	0.009331	0.2325	96.444 %
bartowski	Q5_K_M†	6,852,929,568	6.110	1.008992	0.009362	0.2557	96.430 %
lmstudio	Q6_K (stock)	7,359,259,040	6.562	1.001105	0.006707	0.1642	96.923 %
mradermacher	i1-Q6_K	7,359,260,160	6.562	0.998659	0.005006	0.1360	97.331 %
AaryanK	AK-Q6_K	7,458,301,120	6.651	1.002959	0.004698	0.0995	97.483 %	+0.9 % [-11.4, +17.0] tie
Unsloth	Q6_K	7,458,301,152	6.651	1.003675	0.004592	0.1087	97.476 %
bartowski	Q5_K_L†	7,480,682,528	6.671	1.005524	0.008484	0.2436	96.698 %
AaryanK	AK-Q8_X	9,501,369,536	8.477	1.000676	0.002265	0.0537	98.456 %	-16.5 % [-57.2, +24.7] tie
Unsloth	Q8_0	9,527,502,048	8.500	1.001940	0.002735	0.0586	98.372 %

Intervals are a paired per-token cluster bootstrap (100,000 resamples) over the evaluation chunks, computed on held-out text that played no part in building or selecting any file. Δ is against the closest-sized non-AaryanK file (±3 %, same tensor-set class).

Reading the numbers

The Q6/Q8 ties are honest ties. At near-lossless tiers the absolute divergence is tiny (0.003/0.0007) and the intervals span zero; AK-Q8_X trends −16 % against stock Q8_0 at slightly smaller size, but we call it what the interval says.

Raw benchmark scores and fidelity are different claims. Quantization noise flips borderline benchmark answers in both directions; below 4 bpw, multiple-choice accuracy partly measures how quantization perturbs the model's answer-letter prior rather than knowledge. The fidelity metric that tracks actual closeness to BF16 - harmful flip rate on 2,280 MMLU questions - favours or ties the AK file in every size-matched pair (e.g. 4.0 % vs 7.7 % at the IQ3 point, 5.6 % vs 7.2 % at IQ2).

Does the margin generalise?

The flagship re-measured on six evaluation sets: the lead vs UD-Q4_K_XL is interval-clear on all six, including code, Chinese, and chat-templated text. At 32k context the lead holds or grows: −26 % (flagship), −28 % (IQ3 pair), −38 % (IQ2 pair), ahead in every 4k-token position bin.

Capability - measured, not implied

Same serving for every arm (greedy, thinking off), paired statistics throughout:

MMLU (2,280 q): flagship ties UD-Q4_K_XL (p = 0.45) with fewer harmful flips vs BF16 (1.75 % vs 2.02 %); the IQ3/IQ2 files beat their UD size-peers at p < 1e-4.
HumanEval+ / MBPP+: flagship pass@1 87.2 / 84.7 - identical to UD and within noise of BF16 (86.0 / 85.7). At IQ2: 84.8 vs UD's 75.0.
BFCL tool-calling (500-item frozen slice incl. multi-turn agentic): flagship 69.2 % vs UD 69.6 % vs BF16 71.0 % - statistical ties across the board; multi-turn alone: AK matches BF16 (p = 0.85). Zero malformed tool calls in multi-turn on any arm.
Long-generation stability: 15 fixed prompts × 2,048-token greedy budget on every tier: zero degenerate loops anywhere; cap-hit rates equal to BF16's own.

Tail behaviour

Method

Reference: our own BF16 GGUF, byte-identical to Unsloth's non-MTP BF16 conversion - the comparison measures quantization, not a conversion delta. llama.cpp pinned at 62bf73d2 for every build and every measurement.
Eval: llama-perplexity --kl-divergence, ctx 2048, 81,840 scored tokens per file per slice, identical text and reference for every file.
Statistics: paired per-token cluster bootstrap, 100,000 resamples, per-comparison 95 % intervals, dual-seed stability check; knife-edge results reported as ties.
Selection/verdict separation: allocations were selected on one instrument and verdicts computed on byte-disjoint held-out text that never touched selection.
Reproduced rivals: every competitor file was re-scored on this rig (not transcribed from their cards); the UD flagship reproduction was gated against its historical profile before any verdict ran.
Ship gates: every published artifact rebuilt from scratch with byte-identical tensor payloads; realized per-tensor types audited against intent on every build; inference smoke tests on every tier; capability gates above.
Scope: the language tower is what is measured and quantized; mmproj ships as the stock BF16 projector. KLD values are model-local - compare within this table only.

Per-tensor bit allocation derived separately at each target bit-width using importance data from a diverse in-house calibration set. Base model licence and usage policy unchanged from Qwen/Qwen3.5-9B (Apache-2.0).

AaryanK/Qwen3.5-9B-GGUF

Qwen3.5-9B - GGUF (AK line)