Accelerating BEV Pooling on NVIDIA GPUs for Physical AI Applications
An increasingly common design pattern for autonomous vehicles (AVs), robotics, and spatial AI systems is bird's-eye-view (BEV) perception. BEV models project...
An increasingly common design pattern for autonomous vehicles (AVs), robotics, and spatial AI systems is bird's-eye-view (BEV) perception. BEV models project...
Power can account for 40% of the operating expenses (OpEx) to run an AI factory. Each watt can be spent on overhead, data ingestion, training, or…
As AI systems move from single-turn interactions to coordinated multiagent workflows, low-latency inference becomes increasingly important. Autoregressive LLMs...
AI scientists are emerging as a new interface for scientific computing. These agents can read papers, write code, generate hypotheses, call APIs, inspect files,...
Telecom operators are adopting AI across network operations, customer care, and back-office workflows, but most are still early in the journey to autonomy. In...
The NVIDIA CUDA Core Compute Libraries (CCCL) provides delightful and efficient abstractions for CUDA developers in C++ and Python. It features: Parallel...
When AlphaFold2 revolutionized drug discovery in 2020, its success relied entirely on the roughly 170,000 protein structures collected by scientists since 1971...
Physical AI—robots working autonomously alongside people in factories, warehouses, hospitals, and homes—is arriving faster than most expected. Traditional...
Developers building for AR glasses and wearable devices face an infrastructure gap. The hardware is ready, but creating AI experiences requires integrating live...
Every swipe, transfer, and payment on a modern financial network encodes a pattern of human behavior. Transaction data is one of the richest signals an...