Q3_K_XL is derived from the Q4_X variant, with all ffn_gate and ffn_up experts quantized to 3-bits. [recommended if you can't fit in the Q4_X version].

- IQ1_M : 226.86 GiB (1.90 BPW)
- Q2_K_XL : 322.13 GiB (2.70 BPW)
- Q3_K_XL : 459.94 GiB (3.85 BPW)
- Q8_0-Q4_0 [Q4_X] : 543.62 GiB (4.55 BPW)

For ultra-large MoE models like Kimi, the component that dominates VRAM/RAM usage is the expert block itself. Therefore, our quantization focuses primarily on this critical part, without applying additional precision-mixing on attn or shexp.

👈 Download Guide

huggingface-cli download moxin-org/Kimi-K2-Thinking-Moxin-GGUF --include "*Q3_K_XL*" --local-dir ./Kimi-K2-Moxin

bash

# !pip install huggingface_hub hf_transfer
import os
# os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
from huggingface_hub import snapshot_download
snapshot_download(
    repo_id = "moxin-org/Kimi-K2-Thinking-Moxin-GGUF",
    local_dir = "Kimi-K2-Thinking-Moxin-GGUF",
    allow_patterns = ["*Q8_0-Q4_0*"], # Q3_K_XL, Q2_K_XL, IQ1_M
)

Download Available for huggingface_hub, huggingface-cli, snapshot_download, xet.

Usage

Example of runing gguf with local build of llama.cpp. (llama-cli/llama-server)

👈 Build llama.cpp locally

bash

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp

# -DLLAMA_CURL=OFF if error
cmake -B build -DGGML_CUDA=ON -DBUILD_SHARED_LIBS=OFF 
cmake --build build --config Release -j --clean-first

bash

build/bin/llama-cli -m Kimi-K2-Thinking-Moxin-GGUF/K2-Thinking-IQ1_M/Kimi-K2-Thinking-Moxin-IQ1_M-00001-of-00007.gguf \
  -ngl 99 \
  --temp 1.0 \
  --min-p 0.01 \
  --ctx-size 16384 \ # 4096, 8192

Citation

If this work is helpful, please kindly helpe cite as:

bibtex

@article{chen2025collaborative,
  title={Collaborative Compression for Large-Scale MoE Deployment on Edge},
  author={Chen, Yixiao and Xie, Yanyue and Yang, Ruining and Jiang, Wei and Wang, Wei and He, Yong and Chen, Yue and Zhao, Pu and Wang, Yanzhi},
  journal={arXiv preprint arXiv:2509.25689},
  year={2025}
}

Acknowledgements

This repository builds upon the outstanding work of the following open-source authors and projects:

We sincerely thank them for their excellent contributions to the open-source community.