Fully quantized NVFP4 Qwen3.8-27B with QUASAR QAD

We're releasing a fully quantized NVFP4 version of Qwen3.8-27B. The checkpoint was trained using quantization-aware distillation (QAD) with QUASAR, our new QAT algorithm. We used the original BF16 model as the teacher and distilled the quantized model for 2,446 steps.

The checkpoint supports vLLM on NVIDIA Blackwell GPUs:

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

This model uses an aggressive quantization configuration: every linear layer across all transformer blocks is quantized to NVFP4 (W4A4).

Attention and GDN layers are typically kept at higher precision, such as FP8 or BF16, because quantizing them can cause a significant loss in model quality. With QUASAR, however, the fully quantized checkpoint retains near-BF16 performance. Evaluation results and comparison against other NVFP4 checkpoints:

Paper: https://arxiv.org/abs/2608.13966v1

We'd love to hear your feedback on this checkpoint!