I've made over 20 tests and GPTQv2 is always worse than BitsandBytes 4bit even the 8bit GTPQv2 is worse.

[TByte007]

I comparing GPTQv2 vs BnB using vLLM. For BnB I just use the on the fly quantization. I tried all kinds of combination of options for GPTQv2 and it always comes out worse at accuracy. Even the 8bit is worse then 4bit BnB.
I tested it using "anli" and "hellaswag" on "Nitral-AI/Violet_Magcap-12B" model.
This is part of the results:
ANLI:

GPTQv2 4bit from gptq-quant-ChatGPT-4.1.py WikiText, desc_act: true, gs: 128, damp_percent: 0.1, mse: false
9600/9600 [07:25<00:00, 21.55it/s]
2025-05-09:04:47:54
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=7200), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|-------|------:|------|-----:|------|---|-----:|---|-----:|
|anli_r1|      1|none  |     0|acc   |↑  |0.4830|±  |0.0158|
|anli_r2|      1|none  |     0|acc   |↑  |0.4710|±  |0.0158|
|anli_r3|      1|none  |     0|acc   |↑  |0.4583|±  |0.0144|

GPTQv2 4bit from gptq-quant-ChatGPT-4.1.py WikiText, desc_act: true, gs: 64, damp_percent: 0.1, mse: true
9600/9600 [08:39<00:00, 18.47it/s]
2025-05-09:05:10:15
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=7200), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|-------|------:|------|-----:|------|---|-----:|---|-----:|
|anli_r1|      1|none  |     0|acc   |↑  |0.5160|±  |0.0158|
|anli_r2|      1|none  |     0|acc   |↑  |0.4800|±  |0.0158|
|anli_r3|      1|none  |     0|acc   |↑  |0.4858|±  |0.0144|

Bitsandbytes 4bit
9600/9600 [07:27<00:00, 21.45it/s]
2025-05-09:04:58:01
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=7200), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
| Tasks |Version|Filter|n-shot|Metric|   |Value |   |Stderr|
|-------|------:|------|-----:|------|---|-----:|---|-----:|
|anli_r1|      1|none  |     0|acc   |↑  |0.5220|±  |0.0158|
|anli_r2|      1|none  |     0|acc   |↑  |0.5050|±  |0.0158|
|anli_r3|      1|none  |     0|acc   |↑  |0.5017|±  |0.0144|

Hellswag:

BitsAndBytes 4bit
40168/40168 [31:39<00:00, 21.15it/s]
2025-05-02:05:24:22
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=20,timeout=3600), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag|      1|none  |     0|acc     |↑  |0.6262|±  |0.0048|
|         |       |none  |     0|acc_norm|↑  |0.8091|±  |0.0039|

GPTQv2 8bit from gptq-quant-ChatGPT-4.1.py WikiText
40168/40168 [27:57<00:00, 23.94it/s]
2025-05-08:14:41:55
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=3600), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag|      1|none  |     0|acc     |↑  |0.6061|±  |0.0049|
|         |       |none  |     0|acc_norm|↑  |0.7971|±  |0.0040|

GPTQv2 8bit from gptq-quant-ChatGPT-4.1.py WikiText, desc_act: true, gs: 64
40168/40168 [28:17<00:00, 23.66it/s]
2025-05-08:19:10:18
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=3600), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag|      1|none  |     0|acc     |↑  |0.6067|±  |0.0049|
|         |       |none  |     0|acc_norm|↑  |0.7977|±  |0.0040|

GPTQv2 8bit from gptq-quant-ChatGPT-4.1.py WikiText, desc_act: true, gs: -1
40168/40168 [21:51<00:00, 30.64it/s]
2025-05-08:20:30:24
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=3600), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag|      1|none  |     0|acc     |↑  |0.6072|±  |0.0049|
|         |       |none  |     0|acc_norm|↑  |0.7973|±  |0.0040|

GPTQv2 8bit from gptq-quant-ChatGPT-4.1.py WikiText, desc_act: true, gs: 64, damp_percent: 0.1
40168/40168 [28:09<00:00, 23.78it/s]
2025-05-08:22:01:39
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=3600), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag|      1|none  |     0|acc     |↑  |0.6061|±  |0.0049|
|         |       |none  |     0|acc_norm|↑  |0.7976|±  |0.0040|

GPTQv2 8bit from gptq-quant-ChatGPT-4.1.py WikiText, desc_act: true, gs: 64, damp_percent: 0.1, mse: true
40168/40168 [28:15<00:00, 23.69it/s]
2025-05-09:02:34:33
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=3600), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag|      1|none  |     0|acc     |↑  |0.6062|±  |0.0049|
|         |       |none  |     0|acc_norm|↑  |0.7976|±  |0.0040|

GPTQv2 4bit from gptq-quant-ChatGPT-4.1.py WikiText, desc_act: true, gs: 128, damp_percent: 0.1, mse: false
40168/40168 [24:10<00:00, 27.69it/s]
2025-05-09:04:27:41
local-completions (base_url=http://10.0.0.6:8000/v1/completions,tokenizer=Nitral-AI/Violet_Magcap-12B,num_concurrent=40,timeout=3600), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1
|  Tasks  |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
|---------|------:|------|-----:|--------|---|-----:|---|-----:|
|hellaswag|      1|none  |     0|acc     |↑  |0.5946|±  |0.0049|
|         |       |none  |     0|acc_norm|↑  |0.7872|±  |0.0041|

Those are just some of the tests. I basically tested all the combinations. Non-symmetrical was even worse. I know that I'm probably doing something very wrong but I have no idea what.
Any ideas are be appreciated! (I'm using the GPTQmodel 3.1.0.dev0 from the github from few days ago.)