---
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct/blob/main/LICENSE
base_model_relation: quantized
tags:
- imatrix
- conversational
- qwen3_moe
- ik_llama.cpp
---

## `ik_llama.cpp` imatrix Quantizations of Qwen/Qwen3-Coder-30B-A3B-Instruct
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 three are just test quants for baseline perplexity comparison:
* `bf16` 56.894 GiB (16.007 BPW)
  - Final estimate: PPL = 9.5334 +/- 0.07560
* `Q8_0` 30.247 GiB (8.510 BPW)
  - Final estimate: PPL = 9.5317 +/- 0.07551 (*NOTE* lower than BF16 but didn't use it for "baseline"...)
* `Q4_0` 16.111 GiB (4.533 BPW)
  - Final estimate: PPL = 9.7225 +/- 0.07712

## `IQ5_K` 21.324 GiB (5.999 BPW)
Final estimate: PPL = 9.5930 +/- 0.07614

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ5_K.gguf \
    IQ5_K \
    192
```

</details>

## `IQ4_K` 17.878 GiB (5.030 BPW)
Final estimate: PPL = 9.6023 +/- 0.07613

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ4_K.gguf \
    IQ4_K \
    192
```

</details>

## `IQ4_KSS` 15.531 GiB (4.370 BPW)
Final estimate: PPL = 9.6441 +/- 0.07648

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ4_KSS.gguf \
    IQ4_KSS \
    192
```

</details>

## `IQ3_K` 14.509 GiB (4.082 BPW)
Final estimate: PPL = 9.6849 +/- 0.0768

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-PR735-IQ3_K.gguf \
    IQ3_K \
    192
```

</details>



## `IQ3_KS` 13.633 GiB (3.836 BPW)
Final estimate: PPL = 9.7940 +/- 0.07795

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
    IQ3_KS \
    192
```

</details>

## `IQ2_KL` 11.516 GiB (3.240 BPW)
Final estimate: PPL = 10.0475 +/- 0.08016

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ2_KL.gguf \
    IQ2_KL \
    192
```

</details>

## `IQ2_KT` 9.469 GiB (2.664 BPW)
Final estimate: PPL = 10.1352 +/- 0.08007

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ2_KT.gguf \
    IQ2_KT \
    192
```

</summary>

## `IQ1_KT` 7.583 GiB (2.133 BPW)
Final estimate: PPL = 11.0592 +/- 0.08760

<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-Coder-30B-A3B-Instruct-GGUF/imatrix-Qwen3-Coder-30B-A3B-Instruct-BF16.dat \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-BF16-00001-of-00002.gguf \
    /mnt/raid/models/ubergarm/Qwen3-Coder-30B-A3B-Instruct-GGUF/Qwen3-Coder-30B-A3B-Instruct-IQ1_KT.gguf \
    IQ1_KT \
    192
```

</details>

## Quick Start
#### Full GPU Offload with CUDA
```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)

# Run Server
./build/bin/llama-server \
    --model Qwen3-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
    --alias ubergarm/Qwen3-Coder-30B-A3B-Instruct \
    --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-Coder-30B-A3B-Instruct-IQ3_KS.gguf \
    --alias ubergarm/Qwen3-Coder-30B-A3B-Instruct \
    --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
```

## 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)