Tensor class	Bits	Parameters	Size	Share
Expert FFNs (routed, latent space)	2 (gs 128)	2.72 T	713.2 GiB	93.8%
Shared experts, MoE latent projections, dense MLP	8 (gs 64)	17.5 B	17.4 GiB	2.3%
Attention (KDA + MLA, all projections)	6 (gs 64)	36 B	27.4 GiB	3.6%
Embeddings, `lm_head`	4 (gs 64)	2.4 B	1.2 GiB	0.2%
MoE routers	8 (gs 64)	0.6 B	0.6 GiB	0.1%
Vision tower + projector (unquantized bf16)	—	0.4 B	0.8 GiB	0.1%
Norms, AttnRes projections, gate params (unquantized)	—	—	0.1 GiB	<0.1%

Use with mlx

This model requires Kimi K3 support from mlx-lm PR #1626, which has not yet been merged. Until it is included in an mlx-lm release, install mlx-lm from the PR branch:

bash

pip install git+https://github.com/ml-explore/mlx-lm.git@refs/pull/1626/head
pip install tiktoken

python

from mlx_lm import load, generate

model, tokenizer = load(
    "kernelpool/Kimi-K3-2bit-UVMAX",
    tokenizer_config={"trust_remote_code": True},
    trust_remote_code=True,
)

prompt = "hello"

messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

response = generate(model, tokenizer, prompt=prompt, verbose=True)