GGUF conversion of RadixArk/Qwen3.8-27B-DSpark, a DSpark speculative-decoding drafter for Qwen/Qwen3.8-27B. DSpark support got added very recently in llama.cpp via PR #25173 (merged 2026-07-28).

WARNING: This is highly experimental. I have been unable to get a speedup using DSpark so far. It is very memory hungry for some reason as well. Feel free to experiment with this and see if there are any performance benefits.

Files

File	Size
`Qwen3.8-27B-DSpark-BF16.gguf`	2.73 GB
`Qwen3.8-27B-DSpark-Q8_0.gguf`	1.46 GB

Usage

Requires a llama.cpp build containing PR #25173.

bash

# my config for dspark
--spec-draft-hf erlidev/Qwen3.8-27B-DSpark-GGUF:BF16 \
--spec-type draft-dspark \
--spec-draft-n-max 7 \
-ngld 99

Should work with any Qwen3.8 27B quant.

Conversion

Converted with llama.cpp at 9d57ce456c94d241dde672b2db9cf18879766568.

bash

python convert_hf_to_gguf.py ./Qwen3.8-27B-DSpark \
    --target-model-dir ./Qwen3.8-27B-tokenizer \
    --outtype bf16 --outfile ./Qwen3.8-27B-DSpark-BF16.gguf

llama-quantize Qwen3.8-27B-DSpark-BF16.gguf Qwen3.8-27B-DSpark-Q8_0.gguf Q8_0

Upstream acceptance length

From the source model card:

FP8 target, BF16 draft, block size 7, temp 0.6 / top-k 20 / top-p 0.95.

Workload	Acceptance length
GSM8K	4.57
MATH-500	4.08
MBPP	3.67
HumanEval	3.47
AIME 2025	3.28
MT-Bench	3.10
AIME 2026	3.07
LBPP	3.03
Alpaca	2.95
LiveCodeBench	2.94
Arena-Hard-v2	2.71