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accuracy
float64
0.48
1
accuracy_per_minute
float64
0.06
6.01
label
stringclasses
10 values
mean_completion_tokens
float64
106
3.2k
⌀
mean_wall_s
float64
9.58
628
n
int64
6
200
partial
bool
1 class
reasoning_effort
stringclasses
2 values
suite
stringclasses
6 values
total_wall_s
float64
209
7.31k
ts
timestamp[s]date
2026-08-25 08:47:35
2026-08-26 14:02:14
0.96
6.0098
UD-IQ2_XXS-mtpgpu-effmed
434.2
9.58
50
false
medium
gsm8k
479.22
2026-08-25T08:47:35
0.8
1.6583
UD-IQ2_XXS-mtpgpu-effmed
1,211.5
28.95
20
false
medium
humaneval
578.91
2026-08-25T08:57:15
0.94
3.1823
UD-IQ2_S-effmed
426.5
17.72
50
false
medium
gsm8k
886.15
2026-08-25T09:12:15
1
1.6671
UD-IQ2_S-effmed
862.1
35.99
20
false
medium
humaneval
719.83
2026-08-25T09:24:15
0.94
2.2634
UD-Q2_K_XL-ngl61-effmed
432.6
24.92
50
false
medium
gsm8k
1,245.91
2026-08-25T09:45:15
1
1.5273
UD-Q2_K_XL-ngl61-effmed
693.5
39.29
20
false
medium
humaneval
785.71
2026-08-25T09:58:21
0.98
2.213
bart-IQ2_S-ngl59-effmed
374.8
26.57
50
false
medium
gsm8k
1,328.54
2026-08-25T10:20:45
0.95
1.0458
bart-IQ2_S-ngl59-effmed
800.3
54.5
20
false
medium
humaneval
1,090.06
2026-08-25T10:38:56
0.96
1.6489
UD-IQ3_XXS-ngl54-effmed
409
34.93
50
false
medium
gsm8k
1,746.6
2026-08-25T11:08:17
1
0.9151
UD-IQ3_XXS-ngl54-effmed
769.3
65.57
20
false
medium
humaneval
1,311.34
2026-08-25T11:30:09
0.98
1.2302
UD-IQ3_S-ngl49-effmed
405.7
47.8
50
false
medium
gsm8k
2,389.8
2026-08-25T12:10:15
1
0.6961
UD-IQ3_S-ngl49-effmed
697
86.19
20
false
medium
humaneval
1,723.86
2026-08-25T12:39:00
0.98
0.6565
UD-Q4_K_XL-ngl33-effmed
387.8
89.56
50
false
medium
gsm8k
4,478.22
2026-08-25T13:54:01
1
0.4226
UD-Q4_K_XL-ngl33-effmed
632.8
141.98
20
false
medium
humaneval
2,839.68
2026-08-25T14:41:21
0.48
0.5147
UD-IQ2_XXS-mtpgpu-effmed
2,515.7
55.96
25
false
medium
math25
1,398.97
2026-08-25T15:05:12
0.7
1.9893
UD-IQ2_XXS-mtpgpu-effmed
934.9
21.11
20
false
medium
humaneval_plus
422.25
2026-08-25T15:12:18
0.56
0.4428
UD-IQ2_S-effmed
1,868.6
75.89
25
false
medium
math25
1,897.21
2026-08-25T15:44:09
0.9
1.4009
UD-IQ2_S-effmed
965
38.55
20
false
medium
humaneval_plus
770.95
2026-08-25T15:57:03
0.68
0.3035
UD-IQ2_S-effxhigh
3,195.6
134.44
25
false
xhigh
math25
3,361.07
2026-08-25T18:53:56
0.55
0.5079
UD-IQ2_S-effxhigh
1,589.2
64.98
20
false
xhigh
humaneval_plus
1,299.54
2026-08-25T19:15:39
0.88
0.3059
UD-IQ3_XXS-ngl54-effmed
1,474.4
172.58
25
false
medium
math25
4,314.48
2026-08-25T20:27:50
0.9
0.7269
UD-IQ3_XXS-ngl54-effmed
728.4
74.29
20
false
medium
humaneval_plus
1,485.78
2026-08-25T20:53:10
0.72
0.1477
UD-Q4_K_XL-ngl33-effmed
1,066.1
292.56
25
false
medium
math25
7,313.9
2026-08-25T23:33:37
0.95
0.427
UD-Q4_K_XL-ngl33-effmed
684.5
133.49
20
false
medium
humaneval_plus
2,669.71
2026-08-26T00:18:10
0.8
0.7343
UD-IQ2_S-effxhigh-8k
1,794
65.37
20
false
xhigh
humaneval_plus
1,307.39
2026-08-26T00:40:23
0.8
0.694
EXL3-2.0bpw-effmed
null
69.17
25
false
medium
math25
1,729.15
2026-08-26T06:34:27
0.9
2.2695
EXL3-2.0bpw-effmed
null
23.79
20
false
medium
humaneval_plus
475.87
2026-08-26T06:42:26
0.8
0.3322
bart-IQ2_S-ngl59-effmed
1,969.5
144.48
25
false
medium
math25
3,611.95
2026-08-26T09:08:18
0.9
0.8604
bart-IQ2_S-ngl59-effmed
856
62.76
20
false
medium
humaneval_plus
1,255.27
2026-08-26T09:29:17
0.6667
0.0637
UD-IQ2_S-effmed
105.5
628.12
6
false
medium
needle
3,768.73
2026-08-26T10:32:20
0.875
1.442
UD-IQ2_S-effmed
765.7
36.41
200
false
medium
mmlu
7,281.79
2026-08-26T12:33:42
1
1.725
EXL3-2.0bpw-effmed
null
34.78
6
false
medium
needle
208.7
2026-08-26T12:37:30
0.91
2.148
EXL3-2.0bpw-effmed
null
25.42
200
false
medium
mmlu
5,083.86
2026-08-26T14:02:14

Qwen3.8-27B on 12 GB — result dataset

This dataset mirrors the redistribution-cleared measurements for “Deploying Qwen3.8-27B in 12 GB of VRAM: Accuracy and Throughput Across Quantized Inference Stacks.”

This is the 1.0.1 correction, dated 2026-09-04. Original measurement rows are unchanged; see CORRECTIONS.md. The previous 1.0.0 artifact remains at doi:10.5281/zenodo.22166977. The correction's version DOI is doi:10.5281/zenodo.22314395. Version-specific source and analysis instructions are at https://github.com/matthematics1137/research-artifacts/tree/qwen38-27b-12gb-v1.0.1/papers/2026-qwen38-27b-12gb. The result dataset is https://huggingface.co/datasets/mv1137/qwen38-27b-12gb-results. In prepublication review copies, the reserved DOI and planned tag need not resolve until release; this card alone is not a publication receipt.

This repository contains measurement records under mixed terms rather than a single blanket license. Original measurements and documentation are CC BY 4.0; benchmark-derived fields retain their upstream terms. See LICENSE and THIRD_PARTY_NOTICES.md.

The study measured eight complete artifact–engine deployments on one 80 W RTX 4080 Laptop GPU. It is a single-machine characterization, not a universal model or quantization ranking. Full model outputs were not retained; the per-item files contain scoring fields and short answer excerpts. No model weights are included. Exact upstream weight revisions and hashes are in environment/model_artifacts.json.

The full analysis-only verifier and fixed benchmark subsets live in the canonical GitHub/Zenodo artifact rather than this discovery mirror. From that artifact root, run:

python3 check_claims.py

AI assistance

Claude (Anthropic) and Codex (OpenAI) assisted with evaluation-harness development, experiment execution, evidence auditing, analysis and figure code, and manuscript drafting and revision. Matthew Schwartz directed the work and reviewed and verified the retained outputs against the archived evidence and cited sources, edited the manuscript, and takes full responsibility for the methods, results, interpretation, citations, and released artifacts.

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