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---
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: Qwen/Qwen3-235B-A22B-Thinking-2507
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-235B-A22B-Thinking-2507/blob/main/LICENSE
base_model_relation: quantized
tags:
- imatrix
- conversational
- qwen3_moe
- ik_llama.cpp
---
## `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:
* `bf16` 437.989 GiB (16.003 BPW)
- Final estimate: PPL = TODO
* `Q8_0` 232.769 GiB (8.505 BPW)
- Final estimate: PPL = TODO
## `IQ5_K` 161.722 GiB (5.909 BPW)
Final estimate: PPL = TODO
<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 = TODO
<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>
## `IQ3_K` 106.644 GiB (3.897 BPW)
Final estimate: PPL = TODO
<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>
## `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)
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