---
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](https://github.com/vcruz305/GLM-5.3-Flash-DFlash2-DGX-Spark-recipe)

## Source and attribution

- Original weights: [incoai/GLM-5.3-Flash-DFlash2](https://huggingface.co/incoai/GLM-5.3-Flash-DFlash2)
- Target model: [zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash)
- Method: [DFlash 2: Keep Drafting Parallel](https://inco.ai/blog/dflash2/)
- Code: [z-lab/dflash](https://github.com/z-lab/dflash)

License follows the original: [CC BY-NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/). For commercial use, contact [contact@inco.ai](mailto:contact@inco.ai).

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

```bibtex
@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](https://github.com/ggml-org/llama.cpp/pull/27342), also on [vcruz305/llama.cpp](https://github.com/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](https://huggingface.co/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.

```bash
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

```bash
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`.