Instructions to use incoai/GLM-5.3-Flash-DFlash2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use incoai/GLM-5.3-Flash-DFlash2 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="incoai/GLM-5.3-Flash-DFlash2")

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("incoai/GLM-5.3-Flash-DFlash2")
model = AutoModel.from_pretrained("incoai/GLM-5.3-Flash-DFlash2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use incoai/GLM-5.3-Flash-DFlash2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "incoai/GLM-5.3-Flash-DFlash2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "incoai/GLM-5.3-Flash-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/incoai/GLM-5.3-Flash-DFlash2

SGLang

How to use incoai/GLM-5.3-Flash-DFlash2 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "incoai/GLM-5.3-Flash-DFlash2" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "incoai/GLM-5.3-Flash-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "incoai/GLM-5.3-Flash-DFlash2" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "incoai/GLM-5.3-Flash-DFlash2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use incoai/GLM-5.3-Flash-DFlash2 with Docker Model Runner:
```
docker model run hf.co/incoai/GLM-5.3-Flash-DFlash2
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

GLM-5.3-Flash-DFlash2

Blog | GitHub

This repository contains the DFlash 2 draft model for zai-org/GLM-5.3-Flash. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify.

DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution.

Quick Start

Serve with SGLang:

pip install "sglang[all] @ git+https://github.com/sgl-project/sglang.git@refs/pull/36708/head#subdirectory=python"

sglang serve \
  --model-path zai-org/GLM-5.3-Flash \
  --trust-remote-code \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path incoai/GLM-5.3-Flash-DFlash2 \
  --speculative-draft-attention-backend fa4

See the blog post for more details.

Evaluation

Runtime: SGLang on four NVIDIA GB300 GPUs (TP4), with TRT-LLM DSA and FlashInfer TRT-LLM MoE for the target, FP8 target KV cache, and FlashAttention 4 for DFlash 2 draft attention
Speculation block size: 8 (7 draft tokens per verification step)
Sampling: GLM-5.3-Flash's officially recommended parameters (temperature 1.0, top-p 0.95), with the default Max reasoning effort
Maximum new tokens: 4096
Samples: 128 at concurrency 1; 1,024 at concurrency 8 and 32

We compare autoregressive decoding, GLM-5.3-Flash's native MTP, and DFlash 2. All speculative methods propose seven draft tokens per verification step.

Acceptance Length

Acceptance length is the per-request mean of completion tokens divided by verification steps. Higher is better.

Task	MTP	DFlash 2
GSM8K	5.06	5.78
MATH-500	4.95	5.86
HumanEval	4.70	5.32
MBPP	4.26	4.85
MT-Bench	3.71	4.03

Throughput

Throughput is total output tokens divided by end-to-end wall time. Each cell shows output tok/s (speedup vs. autoregressive).

Concurrency 1

Task	Autoregressive	MTP	DFlash 2
GSM8K	146.8	282.6 (1.93×)	355.4 (2.42×)
MATH-500	157.5	323.2 (2.05×)	438.9 (2.79×)
HumanEval	166.6	323.5 (1.94×)	436.8 (2.62×)
MBPP	168.2	299.5 (1.78×)	402.2 (2.39×)
MT-Bench	169.3	231.1 (1.36×)	293.2 (1.73×)

Concurrency 8

Task	Autoregressive	MTP	DFlash 2
GSM8K	582.6	825.5 (1.42×)	922.9 (1.58×)
MATH-500	794.9	1,267.8 (1.59×)	1,552.3 (1.95×)
HumanEval	897.4	1,533.1 (1.71×)	1,943.5 (2.17×)
MBPP	895.8	1,428.0 (1.59×)	1,821.8 (2.03×)
MT-Bench	864.0	1,084.5 (1.26×)	1,303.2 (1.51×)

Concurrency 32

Task	Autoregressive	MTP	DFlash 2
GSM8K	917.3	1,203.1 (1.31×)	1,318.3 (1.44×)
MATH-500	1,635.6	2,362.7 (1.44×)	2,717.0 (1.66×)
HumanEval	2,089.8	3,511.4 (1.68×)	4,198.4 (2.01×)
MBPP	2,102.0	3,168.5 (1.51×)	3,791.4 (1.80×)
MT-Bench	1,831.5	2,317.0 (1.27×)	2,675.0 (1.46×)

License

This model is released under CC BY-NC-ND 4.0 for research and evaluation. For commercial licensing, contact contact@inco.ai.

Citation

If you find DFlash 2 useful, please cite:

@misc{inco2026dflash2,
  title  = {{DFlash 2: Keep Drafting Parallel}},
  author = {{Inco AI}},
  year   = {2026},
  month  = {August},
  url    = {https://inco.ai/blog/dflash2/}
}

Please also cite the original DFlash paper:

@inproceedings{chen2026dflash,
  title     = {{DFlash: Block Diffusion for Flash Speculative Decoding}},
  author    = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  booktitle = {International Conference on Machine Learning (ICML)},
  year      = {2026}
}