---

base_model: incoai/GLM-5.3-DFlash2

tags:

• transformers
• safetensors
• qwen3
• dflash
• dflash2
• speculative-decoding
• block-diffusion
• draft-model
• sglang
• text-generation
• base_model:zai-org/GLM-5.3
• base_model:finetune:zai-org/GLM-5.3
• license:cc-by-nc-nd-4.0
• text-generation-inference
• region:us

---

GLM 5.3 DFlash2 GGUF

GGUF quantizations of Inco AI DFlash2 draft model for GLM 5.3.

Use with BeeLlama.cpp, a llama.cpp fork with advanced quantization features.

---

GLM-5.3-DFlash2

Blog | GitHub

This repository contains the DFlash 2 draft model for

zai-org/GLM-5.3.

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.

<div align="center">

<img src="assets/dflash2-figure.png" alt="DFlash 2: parallel block drafting with a candidate path selector" width="100%">

</div>

Quick Start

Serve with SGLang:

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

sglang serve \
  --model-path zai-org/GLM-5.3 \
  --tp-size 4 \
  --trust-remote-code \
  --speculative-algorithm DFLASH \
  --speculative-draft-model-path incoai/GLM-5.3-DFlash2 \
  --speculative-draft-attention-backend fa4

DFlash 2 is also supported by vLLM v0.28.0 and later; see

incoai/GLM-5.3-NVFP4 for a

vLLM serving example with the NVFP4-quantized target. See the

blog post for more details.

Evaluation

• Runtime: SGLang on four NVIDIA GB300 GPUs (TP4), with FlashAttention 4 for DFlash 2 draft attention
• Speculation block size: 8 (7 draft tokens per verification step)
• Sampling: GLM-5.3'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'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 | 5.12 | 5.94 |

| MATH-500 | 5.05 | 6.02 |

| HumanEval | 4.85 | 5.48 |

| MBPP | 4.34 | 4.95 |

| MT-Bench | 3.81 | 4.19 |

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 | 113.4 | 292.6 (2.58×) | 366.6 (3.23×) |

| MATH-500 | 113.1 | 297.4 (2.63×) | 383.3 (3.39×) |

| HumanEval | 113.7 | 292.9 (2.58×) | 363.7 (3.20×) |

| MBPP | 113.4 | 266.5 (2.35×) | 336.5 (2.97×) |

| MT-Bench | 113.2 | 206.8 (1.83×) | 244.3 (2.16×) |

Concurrency 8

| Task | Autoregressive | MTP | DFlash 2 |

| :--- | ---: | ---: | ---: |

| GSM8K | 535.3 | 1,094.5 (2.04×) | 1,310.4 (2.45×) |

| MATH-500 | 549.6 | 1,145.6 (2.08×) | 1,409.7 (2.56×) |

| HumanEval | 554.4 | 1,133.8 (2.05×) | 1,360.6 (2.45×) |

| MBPP | 554.2 | 1,049.7 (1.89×) | 1,277.4 (2.31×) |

| MT-Bench | 544.5 | 807.0 (1.48×) | 895.5 (1.64×) |

Concurrency 32

| Task | Autoregressive | MTP | DFlash 2 |

| :--- | ---: | ---: | ---: |

| GSM8K | 1,142.7 | 2,283.6 (2.00×) | 2,694.5 (2.36×) |

| MATH-500 | 1,251.8 | 2,943.2 (2.35×) | 3,559.8 (2.84×) |

| HumanEval | 1,303.1 | 3,016.9 (2.32×) | 3,589.1 (2.75×) |

| MBPP | 1,292.8 | 2,790.2 (2.16×) | 3,380.4 (2.61×) |

| MT-Bench | 1,262.7 | 2,119.5 (1.68×) | 2,345.0 (1.86×) |

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}
}