Kimi-Linear-48B-A3B-Instruct-GGUF

This is a repo for experimental GGUFs for the backend agnostic implementation of the Kimi-Linear model support that requires a llama.cpp from this repo. You can git clone it and compile locally.

git clone https://github.com/ymcki/llama.cpp --branch Kimi-Linear
cd llama.cpp
cmake -B build -DGGML_CUDA=ON
cmake --build build --config Release -j 6

If you have enough VRAM, you can run it purely on your graphics card:

./build/bin/llama-cli -m ~/Kimi-Linear-48B-A3B-Instruct-GGUF/Kimi-Linear-48B-A3B-Instruct.Q4_K_M.gguf -c 8192 -ngl 100 --mmap

Otherwise, you can only load the shared experts and KV cache to your graphics card and the rest to CPU and RAM.

./build/bin/llama-cli -m ~/Kimi-Linear-48B-A3B-Instruct-GGUF/Kimi-Linear-48B-A3B-Instruct.Q4_K_M.gguf -c 8192 -cmoe -ngl 100 --mmap

I am going to only make ggufs without imatrix and ggufs with an imatrix based on c4_en_ja_imatrix.txt for better Japanese performance as bartowski and unsloth will make ggufs with English imatrix anyway.

Base perplexity for f16 gguf is 7.291970 ± 0.048577.

Seems like MLA KV cache can only be run at F16 probably due to itself being a kind of compression. You can use this table to see how much context you can run with a single 24GB card.

As expected, imatrix has no effect on MXFP4_MOE. From this reddit thread, its perplexity is about the same as IQ4_XS but about 6% bigger file size. Here, its perplexity is better than IQ4_XS. This makes it a viable option.