Unkto/DeepSeek-V4-Flash-0731-DSpark-Drafter-IQ1M-IQ2XXS-GGUF

DeepSeek V4 Flash 0731 — compact DSpark drafter (IQ1_M / IQ2_XXS routed experts)

Tiny speculative-decoding drafters for DeepSeek-V4-Flash-0731, built by crushing the routed-MoE experts down to 1-bit (IQ1_M) or 2-bit (IQ2_XXS) using the public routed-MoE importance matrix (Importance matrix (routed-MoE): antirez/deepseek-v4-gguf — imatrix/DeepSeek-V4-Flash-chat-v2-routed-moe-ds4-1p5m.dat).

The DSpark drafter is an auxiliary model — not standalone — it only works together with a DeepSeek V4 Flash 0731 target under llama.cpp --spec-type draft-dspark.

Files

File	Size	Routed experts	Everything else
dspark-DeepSeek-V4-Flash-0731-IQ1_M-exps.gguf	4.46 GiB	IQ1_M	Q8_0 / F32 / BF16
dspark-DeepSeek-V4-Flash-0731-IQ2_XXS-exps.gguf	5.16 GiB	IQ2_XXS	Q8_0 / F32 / BF16
Other variants from the community:
singulared/DeepSeek-V4-Flash-0731-DSpark-GGUF	10.9 GiB "BF16 (source)"	MXFP4 (4-bit)	rest F32/BF16/Q8_0
Lynxpda/DeepSeek-V4-Flash-0731-DSpark-Drafter-Q2_K_S-GGUF	~6.9 GiB	Q2_K	shared experts Q5_K, rest Q6_K/Q8_0/F16/F32/BF16

Performance — DFlash speculative decoding (llama.cpp)

Speculative decoding of DeepSeek-V4-Flash-0731-UD-Q8_K_XL using a draft model (block size 5, --spec-draft-n-max 5).

Table 1 — default --spec-draft-p-min 0.76

Draft model	Main model layers in RAM (--n-cpu-moe)	Draft model layers	Tokens decoded	Draft acceptance rate	Mean draft length	Decode throughput (tok/s)
IQ2XXS	38	VRAM+RAM	54 959	0.898	3.37	14.78
IQ2XXS	38	VRAM	47 667	0.890	3.24	15.27
IQ2XXS	38	VRAM*	13 038	0.880	3.11	15.50
IQ2XXS	38	RAM	15 197	0.906	3.56	13.09
IQ1M	38	VRAM+RAM	45 243	0.884	3.19	14.33
IQ1M	38	VRAM	57 455	0.882	3.23	15.46
IQ1M	40	VRAM	14 961	0.894	3.27	14.80
BF16	38	VRAM+RAM	15 343	0.847	2.98	13.81
BF16	42	VRAM	32 061	0.867	3.19	14.02
DSpark-Drafter Q2_K_S	38	VRAM+RAM	23 326	0.868	3.17	14.30
DSpark-Drafter Q2_K_S	38	VRAM	13 269	0.876	3.14	15.37

Table 2 — --spec-draft-p-min sweep (IQ2XXS, draft experts on CPU)

--spec-draft-p-min	Main model layers in RAM (--n-cpu-moe)	Draft model layers	Tokens decoded	Draft acceptance rate	Mean draft length	Decode throughput (tok/s)
0.46	38	VRAM+RAM	19 506	0.647	3.04	13.32
0.66	38	VRAM+RAM	11 467	0.798	3.03	13.87
0.76	38	VRAM+RAM	54 959	0.898	3.37	14.78
0.80	38	VRAM+RAM	8 793	0.916	3.13	13.99
0.86	38	VRAM+RAM	15 957	0.960	3.20	13.45
0.96	38	VRAM+RAM	23 870	0.995	3.09	12.27

Draft model layers — legend

• VRAM — draft model fully on GPU (default).
• VRAM+RAM — --spec-draft-ngl 99 --spec-draft-override-tensor "exps=CPU" (experts offloaded to CPU).
• RAM — --spec-draft-ngl 0 (draft model fully on CPU).

Notes

• * also sets --spec-draft-type-k q8_0 --spec-draft-type-v q8_0 (quantized draft KV cache).
• Tokens decoded — total generated tokens across the aggregated runs (all metrics are token-weighted averages).
• Draft acceptance rate — fraction of speculative tokens accepted by the target model.
• Mean draft length — average number of accepted draft tokens per step .
• Decode throughput — tokens/s during single-stream autoregressive decoding.

Test environment

Software:  llama.cpp version: 10273 (a6aa6f545) built with GNU 15.2.1 for Linux x86_64
Target:    DeepSeek-V4-Flash-0731-UD-Q8_K_XL (Q8_K_XL)
Drafts:    dspark-DeepSeek-V4-Flash-0731-{IQ1M,IQ2XXS}-exps · BF16 · DSpark-Drafter-Q2_K_S-dflash
Decoding:  speculative, block size 5, --spec-draft-n-max 5, --spec-draft-p-min 0.76
Context:   --ctx-size 266240, single slot (--parallel 1)
Batch:     --n-batch 2048, --n-ubatch 2048
Offload:   --n-gpu-layers 99 (target & draft), --n-cpu-moe 38 (40/42 where noted)
GPUs:      GF4090 + GF3090, --tensor-split 36:6 (6:6 where noted)
CPU pin:   taskset -c 0-15, Intel® Core™ i9-14900K, 196Gb DDR5 5200.

Reproduction

taskset -c 0-15 ./llama.cpp/build/bin/llama-server \
  -m DeepSeek-V4-Flash-0731-UD-Q8_K_XL-00001-of-00005.gguf \
  -md dspark-DeepSeek-V4-Flash-0731-IQ1M-exps.gguf \
  --spec-type draft-dspark --spec-draft-n-max 5 --spec-draft-p-min 0.76 \
  --spec-draft-ngl 99 --ctx-size 266240 --n-cpu-moe 38 \
  --tensor-split 36,6 -ngl 99

How it was built

Selective requantization of the BF16 drafter with llama-quantize, keeping only the 9 routed-expert tensors at low bit and the rest at Q8_0:

llama-quantize \
  --imatrix DeepSeek-V4-Flash-chat-v2-routed-moe-ds4-1p5m.dat \
  --allow-requantize \
  --tensor-type blk.0.ffn_gate_exps.weight=IQ1_M \
  --tensor-type blk.0.ffn_up_exps.weight=IQ1_M \
  --tensor-type blk.0.ffn_down_exps.weight=IQ1_M \
  # ... same for blk.1, blk.2 ...
  dspark-DeepSeek-V4-Flash-0731-BF16.gguf \
  dspark-DeepSeek-V4-Flash-0731-IQ1_M-exps.gguf Q8_0

Credits / sources

• Importance matrix (routed-MoE): antirez/deepseek-v4-gguf — imatrix/DeepSeek-V4-Flash-chat-v2-routed-moe-ds4-1p5m.dat
• BF16 drafter weights (source of this quantization): singulared/DeepSeek-V4-Flash-0731-DSpark-GGUF
• Other DSpark drafter references / mixed recipe: dev7a/DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF, alessandrobologna/DeepSeek-V4-Flash-0731-DSpark-Drafter-GGUF, GaelicThunder, Lynxpda
• Base model: deepseek-ai/DeepSeek-V4-Flash-0731

Weights are redistributed under the base model's release terms (MIT).