IEEE

Lightweight Adaptive Quantization Algorithms for Federated Learning With Heterogeneous Clients

Abstract: Quantization is a common method to improve communication efficiency in federated learning (FL) by compressing the gradients that clients upload. Currently, most application scenarios involve ...
IEEE

Distribution-Adaptive Hierarchical Quantization Enhanced Binary Networks for Spectral Compressive Imaging

Abstract: Hyperspectral image processing faces significant challenges in storage and computation. Snapshot Compressive Imaging (SCI) effectively encodes three-dimensional data into two-dimensional ...
marktechpost

NVIDIA AI Brings Nemotron-3-Nano-30B to NVFP4 with Quantization Aware Distillation (QAD) for Efficient Reasoning Inference

The model is pre-trained on 25T tokens using a Warmup Stable Decay learning rate schedule with a batch size of 3072, a peak learning rate of 1e-3 and a minimum learning rate of 1e-5. The NVFP4 ...
GitHub

Fast kernel library for Diffusion inference with multiple compute backends.

The library provides QuantizedTensor, a torch.Tensor subclass that transparently intercepts PyTorch operations and dispatches them to optimized quantized kernels when available. TensorCoreNVFP4Layout ...