DFlash block-diffusion speculative-decoding drafter for GLM-5.2-FP8 (743B MoE, 39B active). Standard DFlash method (no extensions), trained with SpecForge on the paper-specified data recipe: ~800K samples of Nemotron-Post-Training-v2 + CodeAlpaca (code / math / chat), all responses regenerated by GLM-5.2-FP8, 6 epochs — directly comparable to the DFlash paper and the z-lab drafter series.

Quick Start (SGLang)

bash

python -m sglang.launch_server \
    --model-path zai-org/GLM-5.2-FP8 \
    --speculative-algorithm DFLASH \
    --speculative-draft-model-path UCloud-org/GLM-5.2-FP8-DFlash \
    --speculative-num-draft-tokens 16 \
    --tp-size 8 \
    --trust-remote-code

vLLM v0.20.1+ has native DFlash support and reads this checkpoint directly, no conversion needed. Not yet runtime-verified on our infrastructure; the results below were produced via SGLang.

Evaluation

Mean accepted length & end-to-end speedup

Measured on live SGLang serving (concurrency 1, greedy decoding unless noted).

Benchmark	AL (built-in MTP)	AL (DFlash)	DFlash throughput (tok/s)	Speedup vs vanilla	vs built-in MTP
gsm8k	4.01	4.44	236	2.22x	1.51x
humaneval	4.42	6.43	383	3.44x	1.42x
math500	4.91	7.77	477	4.28x	1.54x
mbpp	5.23	8.04	478	4.29x	1.46x
mtbench	3.71	3.56	220	1.99x	0.93x
ceval	3.65	2.98	177	1.62x	0.93x

Built-in MTP baseline uses the official GLM-5.2 recipe (EAGLE, steps 5 / topk 1 / draft tokens 6). This drafter is code/math-optimized: it delivers 1.4-1.5x over the (already strong) built-in MTP on code and math workloads, while chat and Chinese-language workloads slightly favor built-in MTP (see Limitations).

ceval (Chinese) is the weakest domain — the training corpus is English-dominant (see Limitations).

Also mirrored on ModelScope: UCloud-AILab/GLM-5.2-FP8-DFlash.

Training Details

Target model: GLM-5.2-FP8 (hidden 6144, 78 layers; drafter conditions on target layers [1, 20, 38, 56, 75])
Drafter: 5-layer block-diffusion transformer, block_size 16, 3.7B total parameters (1.8B independently trained; embed/lm_head reused from target, frozen, not trained, included for standalone inference)
Data: JessieWei/GLM-5.2-FP8-nemotron-codealpaca — Nemotron-Post-Training-v2 + CodeAlpaca, ~800K samples (paper-specified recipe), all responses regenerated by GLM-5.2-FP8 (non-thinking mode), max_length 3072
Recipe: 6 epochs, AdamW with update clipping (StableAdamW-style), lr 6e-4 cosine (4% warmup), grad-clip 1.0, num_anchors 512, loss_decay_gamma 7, pure cross-entropy (standard DFlash loss)
Framework: SpecForge (offline hidden-state pipeline), FSDP2

Limitations

Trained on non-thinking-mode regenerated data; speedup under thinking-mode inference has not been evaluated yet.
Trained with max_length 3072: accept length degrades on very long prompts. For long-context workloads, benchmark against the built-in MTP before choosing.
Training corpus is English-dominant: acceptance length on Chinese-language workloads is lower (ceval AL 2.98 vs 4.4-8.0 on English benchmarks).

Acknowledgements

DFlash (z-lab), SpecForge / SGLang (sgl-project). GLM-5.2 by Zhipu AI.

Citation

If you use this model, please cite:

bibtex

@misc{ucloud2026glm52dflash,
  title        = {GLM-5.2-FP8-DFlash: A DFlash Speculative-Decoding Drafter for GLM-5.2-FP8},
  author       = {Wei, Xiaojun and {UCloud AILab}},
  year         = {2026},
  howpublished = {\url{https://huggingface.co/UCloud-org/GLM-5.2-FP8-DFlash}}
}

This model is trained with the DFlash method — please also cite:

bibtex

@misc{chen2026dflash,
  title         = {DFlash: Block Diffusion for Flash Speculative Decoding},
  author        = {Chen, Jian and Liang, Yesheng and Liu, Zhijian},
  year          = {2026}, eprint = {2602.06036}, archivePrefix = {arXiv},
  primaryClass  = {cs.CL}, url = {https://arxiv.org/abs/2602.06036}
}