Qwen3.8-27B NVFP4 GGUF
NVFP4 GGUF quantizations of Qwen/Qwen3.8-27B for llama.cpp, built for native NVFP4 tensor-core math on Blackwell GPUs.
The NVFP4 weights come from our own ModelOpt post-training quantization of the BF16 release, using NVIDIA's dense recipe: NVFP4 at group size 16 on the language-model MLP linears only, calibrated on 512 samples of cnn_dailymail at sequence length 512. Attention (both the full-attention and Gated-DeltaNet linear-attention layers), the vision tower, the MTP block, embeddings and lm_head are left in BF16. That is 192 quantized linears, 64 layers by gate, up and down.
An MTP variant with the speculative draft head is published separately at Qwen3.8-27B-NVFP4-MTP-GGUF.
About LibertAI
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If you want to put this model to work as an autonomous agent without running your own infrastructure, see LiberClaw, Hermes-style agents hosted on Aleph Cloud with LibertAI inference. Free tier: 2 agents, no credit card, 5 minutes to deploy. Open source.
Files
| File | Size | FFN | Other tensors | When to pick |
|---|---|---|---|---|
Qwen3.8-27B-NVFP4-Q4_K_M.gguf | 15.8 GB | NVFP4 | Q4_K_M | Recommended. Fastest variant and the smallest |
Qwen3.8-27B-NVFP4-Q8_0.gguf | 20.0 GB | NVFP4 | Q8_0 | Higher-precision attention and embeddings if you have the VRAM |
Qwen3.8-27B-NVFP4-BF16.gguf | 29.2 GB | NVFP4 | BF16 | Source-precision non-FFN tensors, slower in practice |
mmproj-Qwen3.8-27B-F16.gguf | 0.93 GB | n/a | F16 vision tower | Required for image and video input, reusable across all Qwen3.8-27B GGUFs |
Q4_K_M is both the smallest and the fastest of the three. Inference here is memory-bandwidth bound, so the larger BF16 attention and embedding tensors bottleneck the model even though the FFN math is identical.
Performance
Measured on an NVIDIA RTX 5090 (32 GB, Blackwell, sm_120, CUDA 13.0), llama.cpp build 849798132, compared against a stock Q4_K_M built from the same BF16 source on the same build.
Single-stream, llama-bench, 3 repetitions:
| Variant | Size | pp512 tok/s | tg64 tok/s |
|---|---|---|---|
| NVFP4-Q4_K_M | 14.72 GiB | 5081 | 84.8 |
| NVFP4-Q8_0 | 18.65 GiB | 5169 | 71.8 |
| Stock Q4_K_M | 15.40 GiB | 3947 | 81.2 |
Batched serving, llama-batched-bench, 512 in and 128 out, total throughput:
| Parallel | NVFP4-Q4_K_M | Stock Q4_K_M | Difference |
|---|---|---|---|
| 1 | 398 tok/s | 376 tok/s | +5.9% |
| 4 | 1138 tok/s | 980 tok/s | +16.1% |
| 8 | 1520 tok/s | 1188 tok/s | +28.0% |
| 16 | 2134 tok/s | 1824 tok/s | +17.0% |
Prefill is roughly 29% faster across the range. The token-generation advantage peaks near 8 concurrent requests and narrows at 16, where generation becomes attention and KV-cache bound rather than FFN bound.
Usage
llama-server \
-m Qwen3.8-27B-NVFP4-Q4_K_M.gguf \
--mmproj mmproj-Qwen3.8-27B-F16.gguf \
-ngl 999 -fa on -c 32768 \
--host 0.0.0.0 --port 8080
Drop --mmproj for text-only serving.
Requirements
Native NVFP4 tensor-core math needs a Blackwell GPU (sm_120) and a llama.cpp built with CUDA 12.8 or newer. Build with -DCMAKE_CUDA_ARCHITECTURES=120. On older architectures the NVFP4 tensors still load and run, but without native NVFP4 MMA the speed advantage does not apply.
Caveats
- Calibration was English news text (cnn_dailymail), matching NVIDIA's published recipe. Multilingual and code-heavy workloads were not measured against a held-out set.
- The vision tower is left in BF16 and was not separately evaluated. Vision quality should track the upstream BF16 release closely.
- Benchmarks are from a single RTX 5090. Behavior on other Blackwell parts was not measured.
License and attribution
Inherits Apache 2.0 from Qwen/Qwen3.8-27B. All rights, responsibilities and acceptable-use policies of the upstream license apply.
Quantization performed by LibertAI.