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neroued/Qwen3.6-27B-nvfp4-NInfer
Qwen3.6-27B-nvfp4-NInfer is a image-text-to-text model from neroued. Use it for the image-text-to-text task on the model card, and read the license before you ship it in a product. It is set up for ninfer. The card lists the license as apache-2.0.
This model card is the version-controlled source for neroued/Qwen3.6-27B-nvfp4-NInfer.
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From the Hugging Face model README
This model card is the version-controlled source for neroued/Qwen3.6-27B-nvfp4-NInfer.
The repository contains the NVFP4 representation of
Qwen3.6-27B, converted from the fixed packed weights in
rdtand/Qwen3.6-27B-PrismaSCOUT-Blackwell-NVFP4-BF16-vllm
to the native NInfer .ninfer artifact format. The artifact is
intended only for NInfer; it is not a Transformers checkpoint, Safetensors distribution, or GGUF
file.
The artifact uses the Qwen3.5 Dense architecture with mixed NVFP4/BF16 weights. Actual bindings and activation permissions select the native execution paths, including W4A4 Tensor Core prefill and A16 NVFP4 decode. Text, Vision, MTP, prefix reuse, CLI and serving use the common Engine route.
| Field | Value |
|---|---|
| Filename | qwen3_6_27b_nvfp4.ninfer |
| Size | 18,324,354,820 bytes (17.07 GiB) |
| SHA-256 | 0448262d15df2ae4fda761540c110bc19e7c3b4f43c0938e4d50474429cda083 |
| Container version | 3 |
| Architecture | Qwen3_5ForCausalLM |
| Public model name | qwen3.6-27b |
| Chat template | qwen3_6.jinja; override with --chat-template FILE |
| Template defaults | thinking on; closed-turn reasoning omitted |
| Stored objects | 1,545 (1,539 tensors and 6 resources) |
| NVFP4 tensors | 247 |
The file contains Text, Vision, MTP, the optimized proposal head, and frontend resources. Text linears use the source repository's mixed NVFP4/BF16 allocation; vocabulary and MTP projections use Q8. Vision and speculative weights are loaded only when selected at startup.
Verify a downloaded file with:
printf '%s %s\n' \
'0448262d15df2ae4fda761540c110bc19e7c3b4f43c0938e4d50474429cda083' \
'qwen3_6_27b_nvfp4.ninfer' | sha256sum --check
98dada0
or later, built from source;sm_120a);Already have the official v2 file? Upgrade it locally without downloading the weights again.
NInfer does not provide an install target or packaged binary. See the repository README for source-build dependencies.
hf download neroued/Qwen3.6-27B-nvfp4-NInfer \
qwen3_6_27b_nvfp4.ninfer \
--local-dir models
./build/apps/ninfer models/qwen3_6_27b_nvfp4.ninfer \
--prompt "Explain prefill and decode in three sentences." \
--max-context 32768 \
--max-new 8192 \
--kv-dtype fp8 \
--spec mtp --draft-tokens 3 \
--lm-head-draft
For images, videos, and structured chat history, see the CLI guide.
./build/apps/ninfer-serve models/qwen3_6_27b_nvfp4.ninfer \
--host 127.0.0.1 \
--port 8080 \
--max-context 240000 \
--kv-capacity 240000 \
--max-concurrency 2 \
--kv-dtype fp8 \
--device-state-slots 2 \
--host-state-slots 8 \
--host-kv-mib 8192 \
--spec mtp --draft-tokens 3 \
--lm-head-draft \
--preserve-thinking
Each request has a 240,000-token logical ceiling. The shared 240,000-token Device KV pool admits two active requests when their combined completion reservations fit; either request may use the full pool while running alone. Two extra Device checkpoint slots, eight pinned Host State slots, and 8 GiB of pinned Host KV retain reusable continuations under resource pressure.
See the HTTP serving guide for the API surface and the resource scheduling reference for cache and admission semantics.
The artifact supports:
The single-request serving measurements below were collected on an NVIDIA GeForce RTX 5090 with
CUDA 13.1 compile/runtime and CUDA driver API 13.3. Requests were submitted serially to a persistent
ninfer-serve process with CUDA Graph enabled, a 1,024-token prefill chunk, INT8 group-64 KV cache,
and prefix reuse disabled. Each single-request value is the arithmetic mean ± sample standard
deviation over five fixed seeds; server warm-up completes before the measured requests.
The concurrent campaign uses one 293-token prompt followed by an 8,192-token generation per active request. Each concurrency point starts a fresh server with MTP3, INT8 group-64 KV, CUDA Graphs, a 16,384-token per-request context limit, and prefix reuse disabled. Aggregate throughput includes only complete one-second intervals whose actual decode batch remains equal to C. Each row is one sustained wave.
| C | Steady decode (tok/s) | Speedup vs. C1 | Wave makespan |
|---|---|---|---|
| 1 | 202.4 | 1.00× | 40.46 s |
| 2 | 399.7 | 1.97× | 41.82 s |
| 4 | 699.7 | 3.46× | 47.92 s |
| 8 | 1,146.9 | 5.67× | 58.57 s |
At C=8, the profile sustains 1,146.9 aggregate decode tok/s, or 5.67× its C=1 throughput.
| Prompt tokens | Prefill phase (tok/s) | Server TTFT (ms) | Decode phase (tok/s) |
|---|---|---|---|
| 7,680 | 11,191.5 ± 70.2 | 692.5 ± 4.3 | 86.4 ± 0.5 |
| 64,512 | 6,298.5 ± 97.6 | 10,288.6 ± 159.3 | 78.0 ± 1.2 |
| 130,048 | 4,204.7 ± 14.1 | 31,012.5 ± 104.6 | 71.2 ± 0.2 |
| 260,096 | 2,510.6 ± 16.8 | 103,761.1 ± 698.8 | 59.9 ± 0.3 |
At 7,680 prompt tokens this is 3.48× the prefill throughput of the published groupwise-int profile; at 260,096 prompt tokens it is 1.55×.
Thinking was enabled and the output limit was 65,536 tokens.
| AIME 2026 fixture | Completion tokens | Decode phase (tok/s) | MTP acceptance | MTP tokens/round |
|---|---|---|---|---|
| Problem 1 | 12,053.4 ± 820.9 | 231.0 ± 3.0 | 80.2% ± 1.2% | 3.41 ± 0.04 |
| Problem 15 | 63,109.0 ± 5,426.9 | 213.1 ± 4.2 | 76.3% ± 2.0% | 3.29 ± 0.06 |
| Problem 30 | 57,166.4 ± 9,204.9 | 223.3 ± 1.8 | 81.1% ± 1.5% | 3.43 ± 0.04 |
Each category contains three fixtures and five seeds per fixture (15 samples). Thinking was disabled and the output limit was 4,096 tokens.
| Category | Decode phase (tok/s) | MTP acceptance | MTP tokens/round |
|---|---|---|---|
| Code | 220.3 ± 8.2 | 74.2% ± 4.0% | 3.23 ± 0.12 |
| Story | 148.8 ± 11.6 | 39.2% ± 5.7% | 2.18 ± 0.17 |
| Translation | 213.6 ± 12.2 | 70.5% ± 6.0% | 3.12 ± 0.18 |
| Structured output | 252.2 ± 16.3 | 89.8% ± 8.0% | 3.69 ± 0.24 |
See the full methodology and results, including metric definitions, comparison data, and the exact reproduction command.
The historical serving revision was b3d4d0f50b868711c62432bbd68e746217a2f49a.
See the evaluation workflow
for the serving and runner commands.
The artifact was evaluated through NInfer's OpenAI-compatible serving route with thinking enabled, MTP=3, and a 262,144-token context limit. EvalScope 1.9.0 used 0-shot prompts, rule-based scoring, and one sample per problem with temperature 0.6, top-p 0.95, top-k 20, presence penalty 1.0, and seed 42. All 258 configured samples completed and were scored.
| Benchmark | Accuracy | Correct / total |
|---|---|---|
| AIME 2025 | 93.33% | 28 / 30 |
| AIME 2026 | 93.33% | 28 / 30 |
| GPQA-Diamond | 84.34% | 167 / 198 |
These are single-sample results under the stated NInfer evaluation profile, not pass@k scores.
| Field | Value |
|---|---|
| Base repository | Qwen/Qwen3.6-27B |
| Base revision | 6a9e13bd6fc8f0983b9b99948120bc37f49c13e9 |
| NVFP4 source repository | rdtand/Qwen3.6-27B-PrismaSCOUT-Blackwell-NVFP4-BF16-vllm |
| NVFP4 source revision | 9b5389d4a1e207daab2d47732efea57d7e946dcf |
| Conversion recipe | qwen3_6_27b_nvfp4 |
| Converter repository | https://github.com/Neroued/ninfer |
| Minimum runtime revision | 98dada0e03cb073fd07f905400b5904bc6e82759 |
| Ranking input SHA-256 | c692dc76388132c910547589b4fb4a0503fbd6ad50aaac6a509bbcb192a8afa5 |
The artifact identity, summarized object inventory, and conversion provenance are published in
artifact-manifest.json.
The exact storage contract is maintained in the
v3 container reference.
This NInfer artifact is distributed under the Apache License 2.0. The Qwen3.6-27B base repository and the NVFP4 source repository are also licensed under Apache-2.0. Users remain responsible for complying with the license and applicable laws.