---
title: Qwen3-30B-A3B-Instruct-2507-GGUF
canonical_url: "https://www.modelscope.ai/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF"
md_url: "https://www.modelscope.ai/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF.md"
repository: ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF
last_updated: 2026-03-02
license: apache-2.0
pipeline_tag: text-generation
tasks:
  - text-generation
base_model:
  - Qwen/Qwen3-30B-A3B-Instruct-2507
base_model_relation: quantized
library_name:
  - gguf
  - pytorch
frameworks:
  - pytorch
downloads: 179
stars: 0
tags:
  - imatrix
  - conversational
  - qwen3_moe
  - ik_llama.cpp
  - gguf
---

# Qwen3-30B-A3B-Instruct-2507-GGUF

> Qwen3-30B-A3B-Instruct-2507-GGUF - An open-source model by ubergarm on ModelScope. ikllama.cpp imatrix Quantizations of Qwen/Qwen3-30B-A3B-Instruct-2507 This quant collection REQUIRES ikllama.cpp fork to support the ik's latest SOTA quants and optimizations!…

ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF is a text-generation model on ModelScope. licensed under apache-2.0. derived from Qwen/Qwen3-30B-A3B-Instruct-2507.

- **Repository**: ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF
- **License**: apache-2.0
- **Tasks**: text-generation
- **Base model**: Qwen/Qwen3-30B-A3B-Instruct-2507
- **Tags**: imatrix, conversational, qwen3_moe, ik_llama.cpp, gguf
- **Downloads**: 179
- **Stars**: 0
- **Last updated**: 2026-03-02

Source: https://www.modelscope.ai/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF

---

## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-30B-A3B-Instruct-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*.

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

These first two are just test quants for baseline perplexity comparison:
* `bf16` 56.894 GiB (16.007 BPW)
  - Final estimate: PPL = 7.3594 +/- 0.05170
* `Q8_0` 30.247 GiB (8.510 BPW)
  - Final estimate: PPL = 7.3606 +/- 0.05171

## `IQ5_K` 21.324 GiB (5.999 BPW)
Final estimate: PPL = 7.3806 +/- 0.05170

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

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

# Non-Repeating Layers
token_embd\.weight=iq6_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ5_K.gguf \
    IQ5_K \
    192
```

</details>

## `IQ4_K` 17.878 GiB (5.030 BPW)
Final estimate: PPL = 7.3951 +/- 0.05178

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

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

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ4_K.gguf \
    IQ4_K \
    192
```

</details>

## `IQ4_KSS` 15.531 GiB (4.370 BPW)
Final estimate: PPL = 7.4392 +/- 0.05225

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq4_kss

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ4_KSS.gguf \
    IQ4_KSS \
    192
```

</details>


## `IQ3_K` 14.509 GiB (4.082 BPW)
Final estimate: PPL = 7.4991 +/- 0.05269

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

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

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ3_K.gguf \
    IQ3_K \
    192
```

</details>

## `IQ3_KS` 13.633 GiB (3.836 BPW)
Final estimate: PPL = 7.5512 +/- 0.05307

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq4_ks
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_ks

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

blk\..*\.ffn_down_exps\.weight=iq4_ks
blk\..*\.ffn_(gate|up)_exps\.weight=iq3_ks

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ3_KS.gguf \
    IQ3_KS \
    192
```

</details>

## `IQ2_KL` 11.516 GiB (3.240 BPW)
Final estimate: PPL = 7.7121 +/- 0.05402

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]

# Attention
blk\.(0)\.attn_q.*=q8_0
blk\.(0)\.attn_k.*=q8_0
blk\.(0)\.attn_v.*=q8_0
blk\.(0)\.attn_output.*=q8_0

blk\..*\.attn_q.*=iq5_k
blk\..*\.attn_k.*=iq6_k
blk\..*\.attn_v.*=iq6_k
blk\..*\.attn_output.*=iq5_k

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=q8_0
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=q8_0

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

# Non-Repeating Layers
token_embd\.weight=iq4_k
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ2_KL.gguf \
    IQ2_KL \
    192
```

</details>

## `IQ2_KT` 9.469 GiB (2.664 BPW)
Final estimate: PPL = 8.0270 +/- 0.05698

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]
blk\.(0)\.attn_q.*=iq5_ks
blk\.(0)\.attn_k.*=iq6_k
blk\.(0)\.attn_v.*=iq6_k
blk\.(0)\.attn_output.*=iq5_ks

# Attention
blk\..*\.attn_q.*=iq4_kt
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_kt

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=iq4_kt
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=iq4_kt

blk\..*\.ffn_down_exps\.weight=iq3_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq2_kt

# Non-Repeating Layers
token_embd\.weight=iq4_kt
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ2_KT.gguf \
    IQ2_KT \
    192
```

</summary>

## `IQ1_KT` 7.583 GiB (2.133 BPW)
Final estimate: PPL = 8.7273 +/- 0.06185

<details>

<summary>👈 Secret Recipe</summary>

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

custom="
# 48 Repeating Layers [0-47]
blk\.(0)\.attn_q.*=iq5_ks
blk\.(0)\.attn_k.*=iq6_k
blk\.(0)\.attn_v.*=iq6_k
blk\.(0)\.attn_output.*=iq5_ks

# Attention
blk\..*\.attn_q.*=iq4_kt
blk\..*\.attn_k.*=iq5_ks
blk\..*\.attn_v.*=iq5_ks
blk\..*\.attn_output.*=iq4_kt

# Routed Experts
blk\.(0|47)\.ffn_down_exps\.weight=iq4_kt
blk\.(0|47)\.ffn_(gate|up)_exps\.weight=iq4_kt

blk\..*\.ffn_down_exps\.weight=iq2_kt
blk\..*\.ffn_(gate|up)_exps\.weight=iq1_kt

# Non-Repeating Layers
token_embd\.weight=iq4_kt
output\.weight=iq6_k
"

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

./build/bin/llama-quantize \
    --custom-q "$custom" \
    --imatrix /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/imatrix-eaddario-combined-all-medium-Qwen3-30B-A3B-Instruct-2507-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-30B-A3B-Instruct-2507-GGUF/Qwen3-30B-A3B-Instruct-2507-IQ1_KT.gguf \
    IQ1_KT \
    192
```

</details>

## Quick Start
#### Full GPU Offload with CUDA or or Vulkan (for AMD GPUs)
```bash
# Compile CUDA backend
cmake -B ./build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=ON -DGGML_SCHED_MAX_COPIES=1 -DGGML_CUDA_F16=ON
cmake --build ./build --config Release -j $(nproc)

# Compile Vulkan backend
# Experimental doesn't work with all quant types, need to test some more
# https://github.com/ikawrakow/ik_llama.cpp/discussions/590
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_HIPBLAS=0 -DGGML_VULKAN=1
cmake --build build --config Release -j $(nproc)

# Run Server
./build/bin/llama-server \
    --model Qwen3-30B-A3B-Instruct-2507-IQ3_KS.gguf \
    --alias ubergarm/Qwen3-30B-A3B-Instruct-2507 \
    --ctx-size 32768 \
    -ctk q8_0 -ctv q8_0 \
    -fa -fmoe \
    -ngl 99 \
    --parallel 1 \
    --threads 1 \
    --host 127.0.0.1 \
    --port 8080
```

#### CPU-only Backend
```bash
# Compile
cmake -B build -DCMAKE_BUILD_TYPE=Release -DGGML_CUDA=0 -DGGML_VULKAN=0
cmake --build build --config Release -j $(nproc)

# Run Server
./build/bin/llama-server \
    --model Qwen3-30B-A3B-Instruct-2507-IQ3_KS.gguf \
    --alias ubergarm/Qwen3-30B-A3B-Instruct-2507 \
    --ctx-size 32768 \
    -ctk q8_0 -ctv q8_0 \
    -fa -fmoe \
    -ub 4096 -b 4096 \
    --parallel 1 \
    --threads 8 \
    --host 127.0.0.1 \
    --port 8080 \
    --no-mmap
```

## imatrix note
I used @eaddario's [eaddario-imatrix-corpus-combined-all-medium](https://huggingface.co/datasets/eaddario/imatrix-calibration/blob/main/combined_all_medium.parquet) converted to text like so:
```bash
$ apt-get install duckdb
$ duckdb -ascii -c "SELECT * FROM read_parquet('combined_all_medium.parquet');" > eaddario-imatrix-corpus-combined-all-medium.txt
$ du -h eaddario-imatrix-corpus-combined-all-medium.txt
9.4M    eaddario-imatrix-corpus-combined-all-medium.txt
$ sha1sum eaddario-imatrix-corpus-combined-all-medium.txt
4cde1d5401abdc399b22ab9ede82b63684ad6bb4  eaddario-imatrix-corpus-combined-all-medium.txt
```

## 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)
* [ubergarm-imatrix-calibration-corpus-v02.txt](https://gist.github.com/ubergarm/edfeb3ff9c6ec8b49e88cdf627b0711a?permalink_comment_id=5682584#gistcomment-5682584)
* [eaddario/imatrix-calibration](https://huggingface.co/datasets/eaddario/imatrix-calibration)