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
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.
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
Maxreasoning 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}
}