QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4 · Hugging Face

Source: https://huggingface.co/QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4

https://huggingface.co/QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4#qwen38-27b-nvfp4-trained-with-quasarQwen3.8-27B NVFP4, trained with QUASAR

QUASAR\-QAT/Qwen3\.8\-27B\-QUASAR\-NVFP4is a 4-bit NVFP4 version ofQwen/Qwen3\.8\-27B, produced withQUASAR, a quantization-aware training (QAT) method.

Use it exactly as you would a post-training-quantized (PTQ) build of Qwen3.8-27B: it is a drop-in, lower-precision replacement for the original model, not a fine-tune. The quantized weights are trained by distilling directly from the frozen BF16 original as the teacher, on the teacher’s own output distribution, so the model stays anchored to the original’s behavior instead of drifting away from it. The difference from PTQ is only that the weights arelearnedunder quantization rather than rounded after the fact, which recovers more of the original quality.

📄 Paper:QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction

This model uses the most aggressive quantization settings available for Qwen3.8-27B:every linear layer in every transformer layer is NVFP4 (W4A4)— self-attention, gated delta-net, and MLP alike, 496 of 496. Usually for NVFP4 quantization of this model, attention and gated delta-net layers are kept in higher precision (FP8 or BF16), as the model quality can collapse when they are quantized to NVFP4. However, QUASAR preserves quality similar to the original model even when all linear layers (attention, gated delta net, MLPs) are quantized to NVFP4, which makes this thesmallest available NVFP4 checkpointof Qwen3.8-27B andone of the highest quality.

https://huggingface.co/QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4#how-to-runHow to run

Compatible with vLLM, with no conversion step:

pip install "vllm>=0.27"

vllm serve QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4 \
  --max-model-len 262144 \
  --gpu-memory-utilization 0.85

Requires an NVIDIA GPU with FP4 support (Blackwell, compute capability 10.0+).

https://huggingface.co/QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4#quality-and-size-comparisonQuality and size comparison

We compare against the two other public NVFP4 builds of this model, both of which reach their size by leaving much of the network above 4 bits:unsloth/Qwen3\.8\-27B\-NVFP4(23.4 GB) quantizes only 168 of 496 linears to NVFP4, holding all of self-attention and most of the delta-net at FP8, andInferact/Qwen3\.8\-27B\-NVFP4(26.4 GB) quantizes 304, leaving 192 of the 240 delta-net projections in BF16.

ModelSizeGPQA-Diamond (2 runs, n=396)AIME26 (3 repeats, n=90)Qwen/Qwen3\.8\-27B(BF16 original)55.6 GB0.91411.0000QUASAR\-QAT/Qwen3\.8\-27B\-QUASAR\-NVFP4(this model)19.7 GB0.90911.0000unsloth/Qwen3\.8\-27B\-NVFP423.4 GB0.89390.9778Inferact/Qwen3\.8\-27B\-NVFP426.4 GB0.87630.9667

https://huggingface.co/QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4#trainingTraining

One epoch of loss-aware NVFP4 quantization-aware distillation against the frozen BF16 teacher: global batch size 32, learning rate 1e-6, 2446 steps.

https://huggingface.co/QUASAR-QAT/Qwen3.8-27B-QUASAR-NVFP4#citationCitation

arxiv.org/abs/2608.13966

@article{counathe2026quasar,
  title={QUASAR: Lowering the Loss Floor of Quantization-Aware Training with Loss-Aware Reconstruction},
  author={Counathe, Vincent and Athiwaratkun, Ben and De Sa, Christopher and Zhang, Tianyi},
  journal={arXiv preprint arXiv:2608.13966},
  year={2026}
}