Kimi-K2.5-DFlash

Paper | GitHub | Blog

This model is still under training.

DFlash is a novel speculative decoding method that utilizes a lightweight block diffusion model for drafting. It enables efficient, high-quality parallel drafting that pushes the limits of inference speed.

This model is the drafter component. It must be used in conjunction with the target model moonshotai/Kimi-K2.5. It was trained with a context length of 4096 tokens.

Quick Start

Installation

vLLM:

uv pip install vllm
uv pip install -U vllm --torch-backend=auto --extra-index-url https://wheels.vllm.ai/nightly

SGLang:

uv pip install "git+https://github.com/sgl-project/sglang.git@refs/pull/20547/head#subdirectory=python"

Launch Server

vLLM:

vllm serve moonshotai/Kimi-K2.5 \
  --speculative-config '{"method": "dflash", "model": "z-lab/Kimi-K2.5-DFlash", "num_speculative_tokens": 15}' \
  --attention-backend flash_attn \
  --max-num-batched-tokens 32768

SGLang:

# Optional: enable schedule overlapping (experimental, may not be stable)
# export SGLANG_ENABLE_SPEC_V2=1
# export SGLANG_ENABLE_DFLASH_SPEC_V2=1
# export SGLANG_ENABLE_OVERLAP_PLAN_STREAM=1

python -m sglang.launch_server \
    --model-path moonshotai/Kimi-K2.5 \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path z-lab/Kimi-K2.5-DFlash \
    --speculative-num-draft-tokens 16 \
    --tp-size 8 \
    --attention-backend fa3 \
    --mem-fraction-static 0.9 \
    --trust-remote-code

Tip: For long-context or agentic workloads, add --speculative-dflash-draft-window-size WINDOW_SIZE to enable sliding-window attention for the drafter.

Usage

from openai import OpenAI

client = OpenAI(base_url="http://localhost:30000/v1", api_key="EMPTY")

response = client.chat.completions.create(
    model="moonshotai/Kimi-K2.5",
    messages=[{"role": "user", "content": "Write a quicksort in Python."}],
    max_tokens=4096,
)
print(response.choices[0].message.content)

Early Results

• Thinking: enabled
• Max new tokens: 4096
• Block size: 16
• SGLang results. vLLM results might be different.
• Epoch 1.8

  Dataset	Accept Length
  GSM8K	5.7
  Math500	6.1
  HumanEval	5.7
  MBPP	4.7
  MT-Bench	4.0