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RedHatAI/Kimi-K3-FP8-BLOCK

sourceHugging Faceotherupdated 8d agoView on Hugging Face
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Model Card

Kimi-K3-FP8-BLOCK

Model Overview

  • —Model Architecture: KimiK3ForConditionalGeneration
  • —Input: Text / Image
  • —Output: Text
  • —Model Optimizations:
  • —Weight quantization: FP8
  • —Activation quantization: FP8
  • —Release Date: 2026-07-27
  • —Version: 1.0
  • —Model Developers: RedHatAI

This model is a quantized version of moonshotai/Kimi-K3, using FP8 for both weights and activations, ready for inference with vLLM. See the base model card for full model details and capabilities.

Model Optimizations

This model was obtained by quantizing the weights and activations of the linear operators in moonshotai/Kimi-K3 to FP8 — using block-wise scaling for the weights and dynamic group-wise scaling for the activations — producing an 8-bit (FP8) representation that is ready for inference with vLLM.

Only the weights and activations of the linear operators within the transformer blocks are quantized using LLM Compressor.

Deployment

vLLM Serving

docker run --gpus all \
  --privileged --ipc=host -p 8000:8000 \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  -e GLOO_SOCKET_IFNAME=$IFACE_NAME \
  -e NCCL_SOCKET_IFNAME=$IFACE_NAME \
  -e VLLM_ENABLE_K3_LATENT_MOE_TAIL_FUSION=1 \
  vllm/vllm-openai:kimi-k3 RedHatAI/Kimi-K3-FP8-BLOCK \
  --trust-remote-code \
  --load-format fastsafetensors \
  --gpu-memory-utilization 0.95 \
  --tensor-parallel-size 16 \
  --nnodes 2 \
  --node-rank 0 \
  --master-addr $HEAD_IP \
  --no-enable-flashinfer-autotune \
  --disable-custom-all-reduce \
  --enable-auto-tool-choice \
  --tool-call-parser kimi_k3 \
  --reasoning-parser kimi_k3

Creation

This model was created by applying LLM Compressor with the FP8_BLOCK scheme (model-free post-training quantization, no calibration data required), as shown below.

<details>

python
from compressed_tensors.entrypoints.convert import CompressedTensorsDequantizer

from llmcompressor import model_free_ptq

MODEL_ID = "moonshotai/Kimi-K3"
SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-BLOCK"

ignore = [
    "re:.*embed_tokens.*",
    "re:.*self_attn.*",  # no attention because (q_proj|k_proj|v_proj|b_proj|f_a_proj) are all fused, and `b_proj` has a weight non-divisible by 128
    "re:.*block_sparse_moe\.gate.*",
    "re:.*self_attention_res_proj.*",
    "re:.*mlp_res_proj.*",
    "re:.*output_attn_res_proj.*",
    "re:.*lm_head.*",
    "re:.*vision_tower.*",
    "re:.*mm_projector.*",
]

model_free_ptq(
    model_stub=MODEL_ID,
    save_directory=SAVE_DIR,
    scheme="FP8_BLOCK",
    ignore=ignore,
    converter=CompressedTensorsDequantizer(
        MODEL_ID,
        ignore=ignore,
    ),
    max_workers=7,
    device=[
        f"cuda:{i}"
        for i in range(7)
    ],
)

</details>

License

Both the model weights and code are released under the Kimi K3 License.