How NVIDIA Uses Self-Attention

This article discusses the emulation of the attention mechanism in transformer models using a fully convolutional network, specifically targeting improvements in computer vision tasks.

This article discusses inference optimization techniques for large language models (LLMs), highlighting the challenges and solutions associated with memory and compute efficiency.

The article discusses the intricacies of training Large Language Models (LLMs) using transformer networks, focusing on model architectures, attention mechanisms, and embedding techniques.

The article discusses the structured sparsity feature in the NVIDIA Ampere architecture, particularly focusing on its implementation in deep learning and applications in search engines.

The article discusses the optimizations NVIDIA has made to the BERT model using TensorRT, enabling real-time natural language understanding with significantly reduced latency.