---
base_model:
- Jackrong/Qwopus3.6-27B-v2-MTP-GGUF
- Qwen/Qwen3.6-27B
tags:
- nvfp4
- llama.cpp
- qwopus
- qwopus3.6-27B
- qwen3.6-27B
- mtp
---

This is the second release of the NVFP4 version of [Jackrong's Qwopus3.6-27B-v2-MTP-GGUF](https://huggingface.co/Jackrong/Qwopus3.6-27B-v2-MTP-GGUF).<br>
9-June-2026:  Fixed MTP heads to NVFP4; performance with MTP is much improved (108tk/s tg)
Please note, I am <b>not</B> affiliated; this is my own quantization effort made with my experimental work-in-progress [advanced-gguf-quantizer](https://github.com/michaelw9999/advanced-gguf-quantizer).

More evaluatlions will be underway, this page will be updated when those are complete.<br><br>Feedback on how to improve the quantizer/this quantization is appreciated.<br><br>
For improved performance and quality, try my llama.cpp NVFP4-Repack with MXFP6 from:<br>
  https://github.com/michaelw9999/llama.cpp/tree/nvfp4repack_mxfp6_cuda<br>
These branches are updated regularly.<BR>
NVFP4 repack preloads all tensors into a CUDA tile to boost speed.  It is a tiny bit slower on first load, then provides ~10% prefill boost with a small reduction in token gen seen on larger models, and an increase on smaller models.<br>
However, it also enables NVFP4 <b>input scale</b>, which boosts model correctness.<br><br>

<u>Initial performance results:<br></u>

llama-bench on 5090:<br>
```
qwen35 27B NVFP4 |  15.14 GiB |27.32 B | CUDA | pp512 | 5958.06 ± 6.46 |
qwen35 27B NVFP4 |  15.14 GiB |27.32 B | CUDA | tg128 | 73.72 ± 0.08 |

```
<br>
Perplexity/kld results against wiki2 test:<br>

```
====== Perplexity statistics ======
Mean PPL(Q)                   :   6.959259 ±   0.045420
Mean PPL(base)                :   6.694312 ±   0.042968
Cor(ln(PPL(Q)), ln(PPL(base))):  98.93%
Mean ln(PPL(Q)/PPL(base))     :   0.038815 ±   0.000953
Mean PPL(Q)/PPL(base)         :   1.039578 ±   0.000991
Mean PPL(Q)-PPL(base)         :   0.264947 ±   0.006915

====== KL divergence statistics ======
Mean    KLD:   0.045311 ±   0.000650
Maximum KLD:  19.236860
99.9%   KLD:   2.298393
99.0%   KLD:   0.423422
95.0%   KLD:   0.135200
90.0%   KLD:   0.081522
Median  KLD:   0.017625
10.0%   KLD:   0.000521
 5.0%   KLD:   0.000152
 1.0%   KLD:   0.000024
 0.1%   KLD:   0.000004
Minimum KLD:  -0.000062

====== Token probability statistics ======
Mean    Δp: -0.450 ± 0.016 %
Maximum Δp: 99.955%
99.9%   Δp: 32.792%
99.0%   Δp: 13.999%
95.0%   Δp:  6.515%
90.0%   Δp:  3.788%
75.0%   Δp:  0.717%
Median  Δp: -0.012%
25.0%   Δp: -1.153%
10.0%   Δp: -4.684%
 5.0%   Δp: -8.120%
 1.0%   Δp: -20.878%
 0.1%   Δp: -55.713%
Minimum Δp: -99.221%
RMS Δp    :  6.022 ± 0.055 %
Same top p: 91.085 ± 0.074 %
```
<br>