With the increasing number of users, explosion of data rates, advent of virtualization, and cloud computing technologies, the computing burden on the data center is increasing. Enterprise data centers are at a tipping point—the legacy, hyper-converged data center is giving way to a modern, disaggregated IT infrastructure that is secure and accelerated. Today’s data center is increasingly software-defined for security, networking, storage, and management, and IT looks to accelerated comput...| NVIDIA
The NVIDIA Grace CPU Superchip brings together two high-performance and power-efficient NVIDIA Grace CPUs with server-class LPDDR5X memory connected with NVIDIA NVLink-C2C.| NVIDIA Technical Blog
The NVIDIA CUDA Deep Neural Network library (cuDNN) is a GPU-accelerated library for accelerating deep learning primitives with state-of-the-art performance. cuDNN is integrated with popular deep…| NVIDIA Technical Blog
Operating system of the NVIDIA DGX data center| NVIDIA
NVIDIA and its ecosystem partners are building AI factories at scale for the AI reasoning era — and every enterprise will need one.| NVIDIA Blog
Transformer Engine, part of the new Hopper architecture, will significantly speed up AI performance and capabilities, and help train large models within days or hours.| NVIDIA Blog
The latest TOP500 list reveals 384 systems run on NVIDIA technology, enabling breakthroughs in climate forecasting, drug discovery and quantum simulation.| NVIDIA Blog
We investigate four constraints to scaling AI training: power, chip manufacturing, data, and latency. We predict 2e29 FLOP runs will be feasible by 2030.| Epoch AI
Tensor Cores Features Multi-Precision Computing for Efficient AI inference.| NVIDIA
