kaushikvira/Qwen3.8-27B-thinkingcap-nvfp4full-dflash2-NInfer-v3

ThinkingCap Qwen3.8-27B nvfp4full + DFlash2 for NInfer — v3 container

An all-NVFP4 artifact of bottlecapai/ThinkingCap-Qwen3.8-27B for the NInfer engine, with z-lab's DFlash2 speculative drafter and the indexed proposal head embedded. Native v3 container.

ThinkingCap (BottleCap AI — Osusky, Lindauer, Jirkovsky, Mihal, Platek, Herel, Ihnatchenko, Bartek, Jirak, Kubista, Krus & Tomas Mikolov) is a thinking-efficiency derivative of Qwen3.8-27B: it cuts reasoning tokens by 37% on average (11–66% depending on benchmark) at 85.8% macro accuracy vs the base model's 86.6%, shining on long-context retrieval (−39% thinking, +2.3pp). This artifact brings that checkpoint to Blackwell consumer GPUs via the NInfer stack.

It is the sibling of our production artifact Qwen3.8-27B-swift15-nvfp4full-dflash2-NInfer-v3 — same engine, same quantization pipeline, same DFlash2 drafter. Our same-session A/B (below) kept Swift-1.5 as production and uses this artifact as the token-efficient alternate profile.

Credits & provenance (full chain)

All credit for the model itself goes to the authors below — this artifact is a format conversion + quantization of their work, with no fine-tuning of our own:

What we did (the technique)

Identical pipeline to our Swift-1.5 / Swift-1.0 builds (see the sibling cards):

1. 1.All-NVFP4 quantization with llm-compressor (512 Ultrachat calibration samples, seq 2048, sequential pipeline): NVFP4 W4A4 group-16 on every text projection (MLP gate/up/down all 64 layers, attention Q/K/V/O, GDN inprojqkv/z/out_proj), W8G32 token embedding + output head, official q6/q8 vision allocation, MTP + DFlash2 in BF16. Architecture verified identical to Swift-1.5 pre-build: 64L/5120/24×256/4kv, vocab 248320, 262,144 ctx, 16 full-attention + 48 linear-attention (GDN) layers.
2. 2.Global-divisor normalization (our tool): llm-compressor emits per-module weight_global_scale; the engine's native A4 route requires one contiguous fused parent per attention group (GDN qkvz 16,384 rows, attn qkgv 14,336 rows). We unify each packing group's divisor to D = min(dᵢ) and rescale the E4M3 block scales by D/dᵢ (RNE, shrink-only). 128 groups / 272 member modules; true worst scale re-encode error 6.19% ≤ the 6.25% E4M3 RNE bound (Swift-1.5 was 0.0000% — its within-group divisors were nearly equal; ThinkingCap's differ up to 2.1×, so RNE rounding shows. Decode behaviour is unaffected — verified by the needle + gate results below).
3. 3.v3 conversion with tools/convert (--components text,vision,mtp,dflash2 --proposal).

Artifact

Serving (RTX 5090 32 GB, single GPU)

ninfer-serve qwen3_8_27b_thinkingcap_nvfp4full-dflash2.ninfer \
  --model-id Qwen3.8-27B --max-context 262144 --kv-capacity auto --kv-dtype k8v4 \
  --max-concurrency 4 --default-max-tokens 32768 --prefill-chunk 4096 \
  --temperature 0.9 --min-p 0.05 --spec dflash2 --draft-tokens 7 --lm-head-draft \
  --host-kv-mib 49152 --host-state-slots 16 --vision \
  --default-thinking-budget 16384 --preserve-thinking --image-token-budget 1280

Live capacity on one 5090: weights 18.0 GiB, device KV pool 308,736 tokens (auto), 48 GiB pinned host-KV arena, full 262,144-token context at concurrency 4. The chat template is ThinkingCap's own (carried from the source repo, including its reasoning_effort knob; xhigh is their recommended default).

Benchmarks — same-session A/B vs our Swift-1.5 production artifact

Both sides served by the same engine build, fresh generations, temp 0, no cache reuse, single RTX 5090 (450 W cap, SM clock pinned 2280 MHz).

Reading: ThinkingCap delivers its advertised efficiency on our stack — −17% thinking tokens vs Swift-1.5 with math, speed and long-context retrieval intact (and the fastest decode of any artifact we've built) — but gives back 1.0–3.3pp IFBench. Against the unmodified base Qwen3.8-27B, BottleCap's own H200/vLLM evaluation reports −37% thinking at −0.8pp macro accuracy, so the trade is exactly as advertised by the upstream card. Pick per workload: instruction-following-critical → Swift-1.5; token-cost-dominated long thinking episodes → this artifact. (Both are lossless-swap profiles on the same engine.)

Notes

• —Source license is PolyForm Small Business 1.0.0 + BottleCap personal-use grant (see License below) — distributed in full compliance: license terms, personal-use grant and notices ship with the artifact. It is not Apache-2.0 like our Swift builds.
• —ThinkingCap carries the base Qwen3.8 refusal behavior (not abliterated).
• —Same-session methodology, response caches keyed per-model to avoid reuse.

Measurement conditions

All numbers on this card were measured on a single RTX 5090 32 GB (driver 595.71.05):

• —GPU power cap 450 W (stock 575 W) and SM clock pinned 2280 MHz via the nv-power-limit.service systemd unit — a thermal-efficiency mod (load power ~450 W → ~330 W) with no measurable tok/s loss vs stock.
• —Telemetry correlated with the runs below (70 s thermal-log samples plus a 2 s-sampled instrumented run): peak board draw 375–433 W (cap 450 W), SM clock 2248–2272 MHz (pin 2280), GPU temp 36–69 °C, util 100 % under load.
• —llama-benchy 0.4.0 against the engine's native OpenAI API (not llama.cpp), greedy, mean ± std of 3 runs; tokenizer ukisai/Swift-1.5-Qwen3.8-27b.
• —pp8192 @ dN = throughput of 8,192 new tokens processed on top of N cached context tokens — the agentic-coding profile (long, growing context; small incremental prompts). e2e ttft is end-to-end time to first response chunk.

llama-benchy (single RTX 5090, engine-native API)

kaushikvira Qwen3.8-27B artifact family (single-RTX-5090 serving)

All artifacts are NInfer containers of Qwen3.8-27B derivatives, served by the same engine on the same box. Per-card A/B tables were measured pairwise in the same session; cross-card rows are from different sessions — treat small deltas as indicative.

\* values for the nvfp4full-dflash2.v3 row come from its same-session A/B against swift-abliterated (66.3-vs-65.0 etc. are paired measurements, not independent runs).

Acceptable Use Policy & Legal Notice

By downloading, possessing, or using this artifact you agree to the terms below.

Permitted use

Research, personal/local experimentation, red-teaming, safety research, and evaluating alignment and quantization techniques — subject to all applicable laws and regulations.

Prohibited use

You must not use this model, alone or in any pipeline, to:

• —carry out, plan, or facilitate any activity that is illegal in your jurisdiction;
• —produce content that harms, endangers, defrauds, harasses, or exploits others — including but not limited to weapons, malware, exploitation of minors, targeted harassment, or disinformation presented as fact;
• —provide medical, legal, or financial advice presented as professionally qualified;
• —violate the rights of any person or entity, including intellectual property and privacy rights.

Your responsibility

Model outputs are a capability, not a judgment — never guaranteed trustworthy, factual, or legal. You are solely and fully responsible for every prompt you send, every output you generate, and every use you make of them. The publisher of this artifact (kaushikvira) does not monitor, endorse, or take any part in downstream use.

No warranty / limitation of liability

The artifact is provided "AS IS", WITHOUT WARRANTY OF ANY KIND, express or implied, including merchantability, fitness for a particular purpose, and non-infringement. To the maximum extent permitted by applicable law, the publisher shall not be liable for any claim, damages, or other liability, whether in contract, tort or otherwise, arising from, out of, or in connection with this artifact or its use. Nothing in this card limits liability where limitation is not permitted by law.

Reporting

If you become aware of misuse of this model, report it to the platform where the misuse occurs and to the relevant authorities.

HF-format variant (vLLM / SGLang — JSON-schema structured output)

NInfer does not support JSON-schema output; if you need structured output or the standard vLLM/SGLang stack, use the HF-format NVFP4 weights:

• —[Qwen3.8-27B-thinkingcap-NVFP4-HF](https://huggingface.co/kaushikvira/Qwen3.8-27B-thinkingcap-NVFP4-HF) (ours) — the exact pre-conversion source of this container (17.1 GiB compressed-tensors NVFP4 + MTP head); README has vLLM serve + guided-JSON snippets.

If you benchmark an HF-format variant on your hardware, please share numbers in the repo's Community tab — we collect them on the variant's card.

License

This artifact inherits PolyForm Small Business License 1.0.0 from BottleCap AI's ThinkingCap, plus BottleCap AI's additional personal-use permission. Full texts ship in this repo and apply to every copy distributed from here:

• —`LICENSE` — BottleCap AI license statement + personal-use grant
• —`LICENSE-PolyForm-Small-Business-1.0.0.txt` — full license terms
• —`NOTICE` — attribution + upstream Apache-2.0 Qwen materials
• —`LICENSE-Apache-2.0-Qwen.txt` — upstream Qwen license text

Per the PolyForm terms, we make no sublicense — recipients of this artifact are licensed directly by the original licensor (BottleCap AI) under the same included terms. Anyone who receives a copy gets these terms and the required notice:

Required Notice: Copyright 2026 BottleCap AI (https://bottlecapai.com)

The PolyForm SB copyright + distribution + new-works grants cover individuals and organizations meeting the Small Business condition (< 100 employees and contractors, < $1M revenue, CPI-adjusted); BottleCap's personal-use grant additionally covers individual non-commercial use free of charge. Commercial use outside these conditions requires a license from BottleCap AI (enterprise@bottlecapai.com). Upstream Qwen materials remain Apache-2.0 (see NOTICE).

All model credit to BottleCap AI for ThinkingCap, Qwen team for the base, z-lab for DFlash2, Neroued for the engine. If you use this artifact, please also cite the upstream model:

@misc{ThinkingCap-Qwen3.8-27B,
  title     = {bottlecapai/ThinkingCap-Qwen3.8-27B},
  author    = {Osusky, Adam and Lindauer, Jan and Jirkovsky, Adam and Mihal, Filip and Platek, Ondrej and Herel, David and Ihnatchenko, Luka and Bartek, Vojtech and Jirak, Jiri and Kubista, Daniel and Krus, Frantisek and Mikolov, Tomas},
  year      = {2026},
}