Selective-imatrix TQ3_4S quant of DeepSeek-V4-Flash-0731 (284B MoE / 13B active), tuned per-tensor for coding quality at minimal size. 17% smaller than the previous TQ3_4S release and serves full 1M context on a single box.

Required Runtime

This model uses the custom TQ3_4S tensor type. Stock llama.cpp builds cannot load it — use the TurboQuant fork:

Repo: github.com/turbo-tan/llama.cpp-tq3 (branch main, deepseek4 support; tested build v10413)
This is a standard model — it does not contain an MTP draft block, so no --spec-type draft-mtp flags apply.

Versions

Variant	Size	bpw	Context served	Download
R2_TQ3_4S (this)	91 GB	2.73	1,048,576	Files
TQ3_4S (previous)	110 GB	~3.1	524,288	YTan2000/DeepSeek-V4-Flash-0731-TQ3_4S

Multi-file GGUF: download all 9 shards R2.gguf-00001-of-00009.gguf … 00009-of-00009 (11.8/10.9/10.9/9.5/10.2/10.8/11.1/12.0/9.6 GB) into one folder, then load -00001-of-00009:

python

from huggingface_hub import snapshot_download
snapshot_download(
  repo_id = "YTan2000/R2-DeepSeek-V4-Flash-0731-TQ3_4S",
  local_dir = "R2_TQ3_4S",
  allow_patterns = ["R2.gguf-*", "README.md"],
)

Quick start

Single consumer GPU + system RAM (RTX 3090/3090 Ti + 128 GB DDR4/5)

Full 1M context at 14.3 tok/s — experts run on CPU, attention on GPU:

bash

sudo bash -c 'ulimit -l unlimited; exec ./llama-server \
  -m R2_TQ3_4S/R2.gguf-00001-of-00009.gguf \
  --host 127.0.0.1 --port 8080 \
  -c 1048576 -np 1 \
  -ngl 44 --n-cpu-moe 39 --load-mode mmap+mlock \
  -fa on -ctk q4_0 -ctv tq3_0 \
  --reasoning on --reasoning-budget 256 --reasoning-format deepseek --jinja \
  -t 16 -tb 16 -b 8192 --fit on'

ulimit -l unlimited is required — mlock must pin the full 91 GB or performance silently degrades
--n-cpu-moe 39 routes all MoE experts to CPU; -ngl 44 keeps attention/dense on GPU
compressed KV (-ctk q4_0 -ctv tq3_0) makes 1M context cost only ~14 GB

Single DGX Spark (GB10, 128 GB unified)

bash

llama-server -m R2_TQ3_4S/R2.gguf-00001-of-00009.gguf \
  -ngl 99 -c 1048576 -np 1 --port 8085 \
  -ctk q4_0 -ctv tq3_0 --reasoning-format deepseek --reasoning-budget 256

With the DSpark drafter (--spec-type draft-dspark -md dspark-DeepSeek-V4-Flash-0731-Q8_0.gguf), decode rises to 22.9 tok/s @1M — faster than the previous TQ3_4S at 512K.

Benchmarks

Thinking ON, temp 0, official evalplus scorer.

Benchmark	R2_TQ3_4S (3090 @1M)	R2_TQ3_4S (Spark)	TQ3_4S (Spark, 512K)
HumanEval pass@1	93.3	90.9	94.5
HumanEval+ pass@1	89.0	86.6	90.9
MBPP pass@1	92.6	92.6	91.8
MBPP+ pass@1	77.5	75.9	77.2
Hard86	77/86	76/86	70/86
Decode tok/s @1M	14.3	18.4 (22.9 + drafter)	21.4 @512K

Task-level suite breakdown (raw openai_compat):

Task	R2_TQ3_4S (3090 @1M)
coding	91.7
toolcall	90.0
dataextract	87.1
reasonmath	80.0
instructfollow	77.8
speed	49.4

Caveat: the 3090 suite run used reasoning budget 81,920 (garden server) rather than the 256 used for the Spark baselines.

Tested hardware

1× RTX 3090 24 GB + Ryzen 5950X + 125 GB DDR4-3200 — full 1M, arithmetic gate 6/6
1× DGX Spark (GB10) — full 1M, arithmetic gate 6/6, with and without drafter

Recipe (what "TQ3_4S" means)

Base: deepseek-v4-flash-0731 UD-Q8_K_XL, imatrix-guided requantization:

ffn_gate_exps / ffn_up_exps → IQ2_S (routed experts carry the coding quality)
ffn_down_exps → q2_K/q3_K
attention + dense layers → q4_K/q6_K
attention weights and embeddings → q6_K

Full recipe, tensor-type file and validation logs: github.com/turbo-tan/recipes

License

Base model license applies: deepseek-ai/DeepSeek-V4-Flash. Runtime: llama.cpp-tq3 (MIT).