---
title: Qwen3-235B-A22B-Thinking-2507-GGUF
canonical_url: "https://www.modelscope.ai/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF"
md_url: "https://www.modelscope.ai/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF.md"
repository: ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF
last_updated: 2026-03-18
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
base_model:
  - Qwen/Qwen3-235B-A22B-Thinking-2507
base_model_relation: quantized
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
downloads: 187
stars: 0
tags:
  - imatrix
  - conversational
  - qwen3_moe
  - ik_llama.cpp
  - gguf
---

# Qwen3-235B-A22B-Thinking-2507-GGUF

> Qwen3-235B-A22B-Thinking-2507-GGUF - An open-source model by ubergarm on ModelScope. ikllama.cpp imatrix Quantizations of Qwen/Qwen3-235B-A22B-Thinking-2507 This quant collection REQUIRES ikllama.cpp fork to support the ik's latest SOTA quants and…

ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF is a text-generation model on ModelScope. licensed under apache-2.0. derived from Qwen/Qwen3-235B-A22B-Thinking-2507.

- **Repository**: ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF
- **License**: apache-2.0
- **Tasks**: text-generation
- **Base model**: Qwen/Qwen3-235B-A22B-Thinking-2507
- **Tags**: imatrix, conversational, qwen3_moe, ik_llama.cpp, gguf
- **Downloads**: 187
- **Stars**: 0
- **Last updated**: 2026-03-18

Source: https://www.modelscope.ai/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF

---

## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-235B-A22B-Thinking-2507
This quant collection **REQUIRES** [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp/) fork to support the ik's latest SOTA quants and optimizations! Do **not** download these big files and expect them to run on mainline vanilla llama.cpp, ollama, LM Studio, KoboldCpp, etc!

*NOTE* `ik_llama.cpp` can also run your existing GGUFs from bartowski, unsloth, mradermacher, etc if you want to try it out before downloading my quants.

Some of ik's new quants are supported with [Nexesenex/croco.cpp](https://github.com/Nexesenex/croco.cpp) fork of KoboldCPP.

These quants provide best in class perplexity for the given memory footprint.

## Big Thanks
Shout out to Wendell and the **Level1Techs** crew, the community [Forums](https://forum.level1techs.com/t/deepseek-deep-dive-r1-at-home/225826), [YouTube Channel](https://www.youtube.com/@Level1Techs)!  **BIG thanks** for providing **BIG hardware** expertise and access to run these experiments and make these great quants available to the community!!!

Also thanks to all the folks in the quanting and inferencing community on [BeaverAI Club Discord](https://huggingface.co/BeaverAI) and on [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) for tips and tricks helping each other run, test, and benchmark all the fun new models!

## Quant Collection
Perplexity computed against *wiki.test.raw*. These first two are just test quants for baseline perplexity comparison:

![Perplexity Chart](images/perplexity.png "Chart showing Perplexity improving as BPW increases.")

* `bf16` 437.989 GiB (16.003 BPW)
  - Final estimate: PPL = 4.1898 +/- 0.02367
* `Q8_0` 232.769 GiB (8.505 BPW)
  - Final estimate: PPL = 4.1956 +/- 0.02371

## `IQ5_K` 161.722 GiB (5.909 BPW)
Final estimate: PPL = 4.2213 +/- 0.02391

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq6_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq5_k

# Token Embedding
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/imatrix-Qwen3-235B-A22B-Thinking-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-IQ5_K.gguf \
    IQ5_K \
    192
```

</details>

## `IQ4_K` 134.183 GiB (4.903 BPW)
Final estimate: PPL = 4.2407 +/- 0.02406

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq5_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_k

# Token Embedding
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/imatrix-Qwen3-235B-A22B-Thinking-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-IQ4_K.gguf \
    IQ4_K \
    192
```

</details>

## `IQ4_KSS` 114.093 GiB (4.169 BPW)
Final estimate: PPL = 4.2799 +/- 0.02423

<details>

This one is a little funky just for fun.

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# Attention
blk\..*\.attn_q.*=iq5_ks
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq5_ks

# Routed Experts
blk\.(0|1|2)\.ffn_down_exps\.weight=iq5_ks
blk\.(0|1|2)\.ffn_(gate|up)_exps\.weight=iq4_ks
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# Token Embedding
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/imatrix-Qwen3-235B-A22B-Thinking-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-IQ4_KSS.gguf \
    IQ4_KSS \
    192
```

</details>


## `IQ3_K` 106.644 GiB (3.897 BPW)
Final estimate: PPL = 4.3319 +/- 0.02470

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq4_k
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_k

# Token Embedding
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 1 -m 1 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/imatrix-Qwen3-235B-A22B-Thinking-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-IQ3_K.gguf \
    IQ3_K \
    192
```

</details>

## `IQ3_KS` 101.308 GiB (3.702 BPW)
Final estimate: PPL = 4.3718 +/- 0.02509

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\.(0|1|2|3)\.ffn_down_exps\.weight=iq5_ks
blk\.(0|1|2|3)\.ffn_(gate|up)_exps\.weight=iq4_ks
blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# Token Embedding
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/imatrix-Qwen3-235B-A22B-Thinking-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-IQ3_KS.gguf \
    IQ3_KS \
    192
```

</details>


## `IQ2_KL` 81.866 GiB (2.991 BPW)
Final estimate: PPL = 4.6608 +/- 0.02720

<details>

<summary>👈 Secret Recipe</summary>

```bash
#!/usr/bin/env bash

# Repeating Layers [0-93]

custom="
# Attention
blk\..*\.attn_q.*=iq6_k
blk\..*\.attn_k.*=q8_0
blk\..*\.attn_v.*=q8_0
blk\..*\.attn_output.*=iq6_k

# Routed Experts
blk\..*\.ffn_down_exps\.weight=iq3_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kl

# Token Embedding
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

custom=$(
  echo "$custom" | grep -v '^#' | \
  sed -Ez 's:\n+:,:g;s:,$::;s:^,::'
)

numactl -N 0 -m 0 \
./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/imatrix-Qwen3-235B-A22B-Thinking-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-BF16-00001-of-00010.gguf \
    /mnt/raid/models/ubergarm/Qwen3-235B-A22B-Thinking-2507-GGUF/Qwen3-235B-A22B-Thinking-2507-IQ2_KL.gguf \
    IQ2_KL \
    192
```

</details>

## Quick Start
This example is for a single CUDA GPU hybrid infrencing with CPU/RAM. Check ik_llama.cpp discussions or my other quants for more examples for multi-GPU etc.

```bash
./build/bin/llama-server \
  --model /models/IQ5_K/Qwen3-235B-A22B-Thinking-IQ5_K-00001-of-00004.gguf \
  --alias ubergarm/Qwen3-235B-A22B-Thinking-2507 \
  -fa -fmoe \
  -ctk q8_0 -ctv q8_0 \
  -c 32768 \
  -ngl 99 \
  -ot "blk\.[0-9]\.ffn.*=CUDA0" \
  -ot "blk.*\.ffn.*=CPU \
  --threads 16 \
  -ub 4096 -b 4096 \
  --host 127.0.0.1 \
  --port 8080
```

## References
* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)
* [Getting Started Guide (already out of date lol)](https://github.com/ikawrakow/ik_llama.cpp/discussions/258)