---
base_model: incoai/GLM-5.3-Flash-DFlash2
tags:
- transformers
- safetensors
- qwen3
- dflash
- dflash2
- speculative-decoding
- block-diffusion
- draft-model
- sglang
- text-generation
- base_model:zai-org/GLM-5.3-Flash
- base_model:finetune:zai-org/GLM-5.3-Flash
- license:cc-by-nc-nd-4.0
- text-generation-inference
- region:us
---

# GLM 5.3 Flash DFlash2 GGUF

GGUF quantizations of [**Inco AI DFlash2 draft model**](https://huggingface.co/incoai/GLM-5.3-Flash-DFlash2) for [**GLM 5.3 Flash**](https://huggingface.co/zai-org/GLM-5.3-Flash).

Use with [BeeLlama.cpp](https://github.com/Anbeeld/beellama.cpp), a llama.cpp fork with advanced quantization features.

---


# GLM-5.3-Flash-DFlash2

[Blog](https://inco.ai/blog/dflash2/) | [GitHub](https://github.com/z-lab/dflash)

This repository contains the DFlash 2 draft model for
[`zai-org/GLM-5.3-Flash`](https://huggingface.co/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](https://github.com/sgl-project/sglang):

```bash
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](https://inco.ai/blog/dflash2/) 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](https://creativecommons.org/licenses/by-nc-nd/4.0/)
for research and evaluation. For commercial licensing, contact
[contact@inco.ai](mailto:contact@inco.ai).

## Citation

If you find DFlash 2 useful, please cite:

```bibtex
@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:

```bibtex
@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}
}
```