README.md

---
quantized_by: ubergarm
pipeline_tag: text-generation
base_model: moonshotai/Kimi-K2-Instruct-0905
license: other
license_name: modified-mit
license_link: https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905/blob/main/LICENSE
base_model_relation: quantized
tags:
- mla
- imatrix
- conversational
- ik_llama.cpp
---

## **WIP**

- [x] download fp8 safetensors
- [x] cast fp8 safetensors to bf16 safetensors
- [x] convert to bf16 GGUF
- [x] quantize Q8_0 without imatrix
- [ ] calculate and upload imatrix from Q8_0
- [ ] begin quantizing and releasing

Open a discussion if you have a specific target RAM+VRAM in mind for your rig and I'll see what I can do given the available quants. Cheers!

## `ik_llama.cpp` imatrix Quantizations of moonshotai/Kimi-K2-Instruct-0905
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
Compare with Perplexity of full size `Q8_0` TODO

Final estimate: PPL = TODO

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

### `smol-IQ4_KSS` TODO
Final estimate: PPL = TODO

<details>

<summary>👈 Secret Recipe</summary>

```bash
echo TODO
```

</details>

### `IQ3_KS` TODO
Final estimate: PPL = TODO

<details>

<summary>👈 Secret Recipe</summary>

```bash
echo TODO
```

</details>


### `IQ2_KL` TODO
Final estimate: PPL = TODO

<details>

<summary>👈 Secret Recipe</summary>

```bash
echo TODO
```

</details>

### `IQ2_KS` TODO
Final estimate: PPL = TODO

<details>

<summary>👈 Secret Recipe</summary>

```bash
echo TODO
```

</details>

### `IQ1_KT` TODO
Final estimate: PPL = TODO

<details>

<summary>👈 Secret Recipe</summary>

```bash
echo TODO
```

</details>

## Example Commands
### Hybrid (multiple) CUDA + CPU
```bash
# Two CUDA devices with enough VRAM to offload more layers
# Keep in mind Kimi-K2 starts at 1 unlike DeepSeek at 3 (first dense layers)
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Kimi-K2-Instruct-0905 \
    --ctx-size 32768 \
    -ctk q8_0 \
    -fa -fmoe \
    -mla 3 \
    -ngl 99 \
    -ot "blk\.(1|2|3)\.ffn_.*=CUDA0" \
    -ot "blk\.(4|5|6)\.ffn_.*=CUDA1" \
    -ot exps=CPU \
    --parallel 1 \
    --threads 48 \
    --threads-batch 64 \
    --host 127.0.0.1 \
    --port 8080
```

### CPU-Only (no GPU)
```bash
# compile
cmake -B build -DGGML_CUDA=0 -DGGML_BLAS=0 -DGGML_VULKAN=0
cmake --build build --config Release -j $(nproc)

# run server
# single CPU of a dual socket rig configured one NUMA per socket
numactl -N 0 -m 0 \
./build/bin/llama-server \
    --model "$model"\
    --alias ubergarm/Kimi-K2-Instruct-0905 \
    --ctx-size 98304 \
    -ctk q8_0 \
    -fa -fmoe \
    -mla 3 \
    --parallel 1 \
    --threads 128 \
    --threads-batch 192 \
    --numa numactl \
    --host 127.0.0.1 \
    --port 8080
```

## References
* [ik_llama.cpp](https://github.com/ikawrakow/ik_llama.cpp)