DeepSeek-V4-Flash-0731-TQ3_4S

TurboDeepSeek-V4-Flash

<img src="thumbnail.png" alt="TurboDeepSeek-V4-Flash — TQ3_4S" width="520">

Canonical artifact: DeepSeek-V4-Flash-0731-TQ3_4S_v3

TurboDeepSeek-V4-Flash is the public release name for the TurboQuant GGUF build of DeepSeek-V4-Flash-0731 (256-expert MoE, 6 active + 1 shared, ~21B active params, 43 layers, MLA attention).

The exact file and runtime artifact name is:

• DeepSeek-V4-Flash-0731-TQ3_4S_v3.gguf

Required Runtime

This release requires the TurboQuant runtime fork:

• https://github.com/turbo-tan/llama.cpp-tq3

Stock llama.cpp builds without TurboQuant support cannot load the custom TQ3_4S tensor type. This is a standard (non-MTP) release — the GGUF does not contain an MTP draft block. A community DSpark drafter exists for this base model but cannot be loaded by this runtime — see Speculative Decoding below, where that was measured rather than assumed.

Parent Model

• Upstream parent: deepseek-ai/DeepSeek-V4-Flash-0731
• Format conversion and TurboQuant packaging: turbo-tan/llama.cpp-tq3

Files

• DeepSeek-V4-Flash-0731-TQ3_4S_v3.gguf — main model, 102.75 GiB (110,322,537,056 bytes)
• thumbnail.png — model card image
• benchmarks.html — full interactive benchmark page (perplexity, speed, Hard86, evalplus, six task suites)
• benchmark.png — benchmark summary (three-way vs IQ3_XXS and Laguna S2.1; note it reports reasoning-off figures for all three engines, labelled on the image, while the headline figures above are the recommended reasoning-on profile)

Recommended Runtime

Enable reasoning. Serving with reasoning disabled (--reasoning off) produces a confirmed, reproducible arithmetic bug (see Known Behavior below) — this is NOT a quantization artifact, it reproduces identically on an independently-built IQ3-XXS quant of the same base model, and is fixed by the <think> trigger itself (confirmed down to a 1-token reasoning budget).

./build/bin/llama-server \
  -m DeepSeek-V4-Flash-0731-TQ3_4S_v3.gguf \
  --host 0.0.0.0 --port 8085 \
  -c 524288 -np 1 -ngl 99 -fa on -b 4096 -ub 2048 \
  -ctk q4_0 -ctv tq3_0 --threads 20 \
  --reasoning on --reasoning-budget 256 \
  --reasoning-format deepseek --jinja

All figures below are validated at this exact 512K serving config (-c 524288 -np 1 -ctk q4_0 -ctv tq3_0, reasoning-on, budget 256) on NVIDIA GB10 (DGX Spark), 2026-08-05. The full battery (Hard86, HumanEval, MBPP, Benchloop) shows zero regression versus the 36K context baseline — 512K context costs nothing on quality for this build.

-ctk q4_0 was measured on 2026-08-05 at 512K: Hard86 70/86, HumanEval 94.5 / 90.9, MBPP 91.8 / 77.2, at roughly half the K-cache footprint (2.9 GB vs 5.4 GB at 512K).

Perplexity — wikitext-2, the standard setting. Measured 2026-08-05, wiki.test.raw, -c 512 --chunks 60, which is the configuration published figures are normally quoted at:

PPL falls with longer context, so these are not comparable to numbers quoted at other context lengths — compare only like for like.

The K-cache setting is a real trade-off. Measured with only the K-cache dtype changed (-ctv tq3_0 held constant, same corpus/context/chunks):

-ctk q8_0  ->  PPL 5.98        -ctk q4_0  ->  PPL 6.09       (+1.8%)

So -ctk q4_0 costs roughly 1.8% perplexity and saves roughly 2.5 GB of K-cache at 512K (2.9 GB vs 5.4 GB). Task benchmarks (Hard86, HumanEval, MBPP) do not resolve a shift this small — perplexity does. The headline figures above were measured at q4_0 and carry this cost.

If you have the memory, prefer -ctk q8_0. Use q4_0 when the K-cache saving is what makes your context length fit at all — which, at 512K on a 121 GB box, it is.

Note also that the Hard86/evalplus comparison between the 512K and 36K profiles below varies two things at once (context length and K-cache dtype), so no difference between those columns can be attributed to either alone.

Perplexity cost of the K-cache change, measured on this artifact:

Optimized flash-attention kernels exist for quantized K/V, so there is no decode-speed penalty. Keep -ctv tq3_0 (a larger V-cache measurably worsened quality in testing); do not raise K or V cache types without re-validating.

512K fits only at -np 1 on a 121 GB box (weights 102.7 GB + KV + compute). For multi-slot serving, drop -c accordingly (e.g. -c 147456 -np 4 = 4×36K slots was the validated 36K config).

--reasoning-budget 256 is a deliberate cap, not the default unrestricted (-1): unbounded reasoning burned 6000+ think-tokens on some coding tasks (about 6 min/task); budget=256 (about 150-200 think words) keeps latency reasonable while still triggering the fix. Budget=1 is sufficient to fix the arithmetic bug but backfires on coding tasks (reasoning dumps into visible content instead of the reasoning_content field) — do not go below the tested 256 floor without re-validating on coding tasks specifically.

⚠️ At a capped reasoning budget, a concise system prompt is REQUIRED

This is load-bearing, not a style preference. At --reasoning-budget 256 the model must be told not to deliberate in its visible answer, or it spends the capped allowance thinking and is cut off before emitting any code.

Use a system prompt such as:

Answer concisely. Do not deliberate, plan, or write meta-commentary in your response; give the direct answer only.

Measured on HumanEval (164 tasks), same model, same budget, same harness — the only difference is the system prompt:

If you deploy at a capped budget with an ordinary prompt, expect empty or truncated output. Either use a prompt of the form above, or raise --reasoning-budget (at the cost of the latency noted above). All benchmark figures in this card were produced with the concise prompt.

Then request:

curl -s http://127.0.0.1:8085/v1/chat/completions \
  -H 'Content-Type: application/json' \
  -d '{"model":"test","messages":[{"role":"user","content":"What is 81 / 9?"}],"max_tokens":512,"temperature":0}'

Expected assistant content: 9 (not 81; see Known Behavior).

Speculative Decoding (DSpark) — NOT SUPPORTED by this runtime

Measured 2026-08-05 on this exact quant and runtime: it does not load.

The community DSpark drafter (bleysg/DeepSeek-V4-Flash-DSpark-drafter-GGUF, DSpark-drafter-Q2K-Q8-0731.gguf, 6.5 GiB) declares model architecture deepseek4-dspark, which the required runtime cannot load:

error loading model: unknown model architecture: 'deepseek4-dspark'
common_speculative_init_result: failed to load draft model
srv  llama_server: exiting due to model loading error

llama.cpp-tq3 has no support for that architecture, so -md ... --spec-type draft-dspark fails at startup — the server does not come up at all. Third-party speedup figures for this drafter come from a different engine and cannot be reproduced here.

Measured baseline for reference (same paired harness, drafter arm failed):

If DSpark support lands in llama.cpp-tq3, this section should be re-measured with the paired A/B harness (scripts/measure_dspark_spec_dsv4.sh) rather than re-citing an external number.

Known Behavior — Arithmetic "dividend echo": NOT REPRODUCIBLE (2026-08-05)

A "dividend echo" bug has been reported for this model family — that under --reasoning off it answers division by echoing the dividend (81 / 9 -> "81"). It does not reproduce on this build. Testing with --reasoning off --reasoning-budget 0 and one variable changed per run gave the correct answer in every configuration:

15/15 correct. That includes the exact serving recipe published above.

This does not prove the behaviour was never real — it was reported against two independently-built quants, and a runtime fix is a plausible explanation. It means the issue is not reproducible on this build. If you can reproduce it, please report your exact flags.

Reasoning-on is still the recommended profile, on the benchmark evidence above — it is the configuration all published figures were measured at.

Known Behavior — reasoning budget can silently produce EMPTY output

Independently confirmed on 2026-08-05, and more likely to affect you than the above. At --reasoning-budget 256, a hard task can consume the entire allowance on reasoning and emit nothing — even with the concise system prompt. Observed on a Hard86 diff/patch task:

completion_tokens : 258        <- exactly the 256 budget, +2
content           : ''         <- empty
finish_reason     : stop
reasoning         : cut off mid-sentence, having correctly identified the approach

Every other task in that run used 345-2027 tokens and produced content; this was the only one pinned at the cap. The model knew the solution and ran out of budget before writing it.

The concise system prompt reduces this risk but does not eliminate it. If you see empty responses on hard prompts, raise --reasoning-budget (at the latency cost noted above) rather than assuming the model failed.

Benchmark Summary

Three-way comparison at reasoning-off for all three engines (labelled on the image). The headline figures below are the recommended reasoning-on profile, so the two differ by design. Full interactive version: benchmarks.html.

Two validated serving profiles. The recommended runtime is reasoning-ON (see Recommended Runtime above); reasoning-OFF has the arithmetic bug documented under Known Behavior, so the reasoning-OFF row is provided only for reference/comparison — use reasoning-ON.

Recommended profile — 512K, reasoning-ON, budget 256 (--reasoning on --reasoning-budget 256, -c 524288 -np 1, -ctk q4_0 -ctv tq3_0, concise system prompt, max_tokens 16384, GB10/DGX Spark, validated 2026-08-05):

* Benchloop 79.5 measured at 512K with -ctk q8_0 (2026-08-05, before the K-cache type change); coding 100 / instructfollow 87.8. ctk q4_0 was afterwards validated quality-neutral on Hard86 + Evalplus. † Custom 15-suite and 36K baselines measured at -c 147456 -np 4 (4×36K), -ctk q8_0 -ctv tq3_0, otherwise identical profile. ‡ Arithmetic gate validated at 512K with -ctk q8_0, budget 256.

Headline: 512K context shows zero quality regression versus 36K on every suite measured at both lengths. Reasoning-ON improves code benchmarks over reasoning-OFF (HumanEval+ 88.4 → 90.9) and fixes the arithmetic bug, at the cost of some Hard86 sub-tests where the concise-prompt budget trades depth for brevity.

Reference profile — reasoning-OFF (512K context, -ctk q8_0 -ctv tq3_0, np=1 — DO NOT USE for arithmetic, see Known Behavior):

Decode throughput (reasoning-ON, budget=256, GB10): 18–19.6 tok/s single-stream at 512K context (measured 18.1–19.0 tok/s across the 512K battery, 2026-08-05); 19.6 tok/s measured earlier at -c 32768. Throughput is context-insensitive at these lengths — decode is weights-bandwidth-bound, not KV-bound.

Serving stability at 512K (GB10, 121 GB unified memory)

512K serving leaves little headroom (weights 102.7 GB ≈ 85% of the box). The validated battery completed with the server sitting below 1% free memory for hours — zero OOM kills, zero restarts — under this hardening:

1. Run llama-server as a systemd system unit (not nohup over SSH — user-slice cgroup teardown kills long-lived processes; see below).
2. Add zram swap (~32 GB, zstd) as a pressure valve, and retune earlyoom's swap threshold accordingly (-s 10, not the common -s 100 which would kill on any swap use).
3. Put llama-server in earlyoom's --avoid list (protects the server; note earlyoom will then shoot system services instead under extreme pressure — headroom is the real fix).

Tested Hardware

• NVIDIA GB10 (DGX Spark), unified memory, ARM
• Confirmed working identically on two separate GB10 boxes over both standard network and a dedicated QSFP fabric link (transfer only; inference tested single-box)

License

Use is subject to the base model license (MIT, deepseek-ai/DeepSeek-V4-Flash-0731) and the license terms of the runtime components used to run the GGUF.