---

license: cc-by-nc-nd-4.0

base_model: incoai/GLM-5.3-Flash-DFlash2

library_name: gguf

pipeline_tag: text-generation

tags:

- gguf

- dflash

- dflash2

- speculative-decoding

- glm

---

GLM-5.3-Flash-DFlash2-GGUF

Community GGUF of Inco AI's DFlash 2 draft model for GLM-5.3-Flash.

This is not a standalone language model. It only drafts tokens for a GLM-5.3-Flash target under speculative decoding.

Spark serve recipe: vcruz305/GLM-5.3-Flash-DFlash2-DGX-Spark-recipe

Source and attribution

• Original weights: incoai/GLM-5.3-Flash-DFlash2
• Target model: zai-org/GLM-5.3-Flash
• Method: DFlash 2: Keep Drafting Parallel
• Code: z-lab/dflash

License follows the original: CC BY-NC-ND 4.0. For commercial use, contact contact@inco.ai.

If you use this GGUF, please cite Inco AI's DFlash 2 writeup and the DFlash paper:

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

@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}
}

Files

| File | Type | Size | Notes |

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

| GLM-5.3-Flash-DFlash2-BF16.gguf | BF16 GGUF | 2.191 GiB (2,352,022,432 B) | 81 tensors, arch dflash |

GGUF metadata from this convert: dflash.block_size=8, conv_kernel_size=2, conv_group_size=16, selector_rank=256, selector_top_k=16, target_layers=[6,15,25,34,43]. Vocab is the GLM-5.3 tokenizer (154880).

Q8_0 / Q4_K_M drafts were measured on a Spark and are not in this repo.

Run

Needs llama.cpp with DFlash 2 (grouped dynamic conv + candidate selector). That is ggml-org/llama.cpp#27342, also on vcruz305/llama.cpp main / glm5next-mtp at 6f5ac9a (+ aarch64 cmath 4a06ec6). Pair with a GLM-5.3-Flash target GGUF such as vcruz305/GLM-5.3-Flash-GGUF.

--spec-draft-n-max clamps to 7 (block_size - 1). Do not combine with --spec-type draft-mtp on the same server.

hf download vcruz305/GLM-5.3-Flash-DFlash2-GGUF GLM-5.3-Flash-DFlash2-BF16.gguf --local-dir GLM-5.3-Flash-DFlash2-GGUF

llama-server \
  -m GLM-5.3-Flash-Q2_K.gguf \
  -md GLM-5.3-Flash-DFlash2-GGUF/GLM-5.3-Flash-DFlash2-BF16.gguf \
  --spec-type draft-dflash --spec-draft-n-max 7 --spec-draft-p-min 0.30 \
  -fa on -ctk q8_0 -ctv q8_0 --jinja \
  -c 98304 -np 1 --no-kv-unified --fit off

Measured (one DGX Spark GB10, 2026-08-27)

Target: GLM-5.3-Flash-Q2_K.gguf. Tool: llama-speculative-simple, greedy, seed 42.

Unique prompt "The capital of France is" (n=64, FA on, no q8 KV): 17.58 t/s, accept 31.41%.

Repetitive bench file, FA + q8 KV, -c 2048, n=128:

| draft | n_max | t/s | accept |

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

| MTP-3 control | 3 | 28.23 | 73.2% |

| DFlash2 BF16 | 7 | 41.40 | 94.4% |

| DFlash2 Q4_K_M | 7 | 43.43 | 94.4% |

Do not quote 94% as a model score — that file is a repeated sentence.

Ctx ladder (Q4_K_M, n_max=7, p_min=0.30, FA+q8, n=64):

| ctx | t/s | accept |

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

| 8,192 | 39.58 | 89.9% |

| 32,768 | 39.53 | 89.9% |

| 65,536 | 39.71 | 89.9% |

| 98,304 | 38.86 | 89.9% |

96k is the last measured OK (114,820 MiB of 124,610). 114k/128k not re-run for DFlash2.

Convert

python convert_hf_to_gguf.py incoai/GLM-5.3-Flash-DFlash2 \
  --target-model-dir zai-org/GLM-5.3-Flash-BF16 \
  --outtype bf16 \
  --outfile GLM-5.3-Flash-DFlash2-BF16.gguf

--target-model-dir is tokenizer + config.json only. Converted with vcruz305/llama.cpp 6f5ac9a.