michaelw9999/Qwen3.6-35B-A3B-MXFP6-GGUF overview
Changed GGML TYPE to 50, latest mxfp6 build is required This is an proof of concept/work in progress Qwen3.6 35B A3B quantized into MXFP6.<BR It was quantized …
Runs locally from ~26.47 GB disk (32 GB+ VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
| File | Type | Quantization | Size | Link |
|---|---|---|---|---|
| Qwen3.6-35B-A3B-MXFP6.gguf | GGUF | GGUF | 26.47 GB | Download |
Model Details
| Model ID | michaelw9999/Qwen3.6-35B-A3B-MXFP6-GGUF |
|---|---|
| Author | michaelw9999 |
| Pipeline | — |
| License | — |
| Base model | Qwen/Qwen3.6-35B-A3B |
| Last modified | 2026-07-28T10:25:11.000Z |
Model README
---
base_model:
- Qwen/Qwen3.6-35B-A3B
tags:
- mxfp6
- MXFP6
- llama.cpp
- gguf
- qwen3.6
---
Changed GGML TYPE to 50, latest mxfp6 build is required
This is an proof of concept/work in progress Qwen3.6-35B-A3B quantized into MXFP6.<BR>
It was quantized with my
<B>still experimental</B> <A HREF="https://github.com/michaelw9999/advanced-gguf-quantizer">advanced-gguf-quantizer</A> tool.<BR>
This GGUF will ONLY work on llama.cpp. The CPU only PR is posted here:
https://github.com/ggml-org/llama.cpp/pull/22671
The PR runs very slowly because that is for the initial implementation without GPU support.
You may preview the very fast POC CUDA version from my fork:
https://github.com/michaelw9999/llama.cpp/tree/mxfp6-cuda
To merge into your existing llama.cpp installation:
git remote add mxfp6 https://github.com/michaelw9999/llama.cpp
git fetch mxfp6
git merge mxfp6/mxfp6-cuda
cmake -B build -DGGML_CUDA=ON
cmake --build build -j
Or to install fresh:
git clone -b mxfp6-cuda https://github.com/michaelw9999/llama.cpp
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build -j
NOTICE:
This is my own work and is experimental and unofficial.
The CUDA version is not part of any llama.cpp PR (yet). This is not associated with NVIDIA in anyway.
Very likely, any future MXFP6 design will not be compatible with this implementation.
For Qwen3.6-35B, MXFP6 is almost as fast as NVFP4 on prefill.
Using FP8 for activations, it is faster than NVFP4 on tokengen.
Feedback is both requested and encouraged so I can make further improvements into future llama.cpp PRs.
The NVFP4/MXFP6 quantizer is still being improved and will be posted in the future.
Please let me know if you want to see a specific model turned into MXFP6.
I will create an MTP enabled version soon.
MXFP6: Final estimate: PPL = 6.7890 +/- 0.04420
Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32606 MiB
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen35moe 35B.A3B MXFP6 - E2M3 | 26.46 GiB | 34.66 B | CUDA | 99 | pp512 | 8094.43 ± 49.53 |
| qwen35moe 35B.A3B MXFP6 - E2M3 | 26.46 GiB | 34.66 B | CUDA | 99 | tg128 | 188.10 ± 3.20 |
Device 0: NVIDIA GeForce RTX 5090, compute capability 12.0, VMM: yes, VRAM: 32606 MiB
| model | size | params | backend | ngl | test | t/s |
| ------------------------------ | ---------: | ---------: | ---------- | --: | --------------: | -------------------: |
| qwen35moe 35B.A3B NVFP4 | 21.48 GiB | 34.66 B | CUDA | 99 | pp512 | 8220.18 ± 57.89 |
| qwen35moe 35B.A3B NVFP4 | 21.48 GiB | 34.66 B | CUDA | 99 | tg128 | 159.53 ± 0.82 |Run michaelw9999/Qwen3.6-35B-A3B-MXFP6-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models