NVIDIA, originally known for graphics, has transformed the GPU into an Accelerated Compute development platform. GPU Deep Learning has ignited modern AI, with GPUs as the compute engine for servers from Data Center to Edge. NVIDIA GPU Accelerated Computing will enable a new wave of AI applications and business outcomes.
Artificial Intelligence, Deep Learning and GPU Accelerated Analytics present a massive opportunity to dramatically improve products and services in all industries, which is why more than 19,000 organizations have begun to use deep learning. Organizations need an accelerated computing platform that enables them to develop GPU Applications to accelerate insights and outcomes, turbocharge their data center, and innovate for the future.
NVIDIA’s Deep Learning SDK provides powerful tools and libraries for designing and deploying GPU-Accelerated Deep Learning applications. It includes libraries for deep learning primitives, inference, video analytics, linear algebra, sparse matrices and multi-GPU communications.
NVIDIA’s Deep Learning Frameworks offer building blocks for designing, training and validating deep neural networks through a high level programming interface. Widely used Deep Learning frameworks such as Caffe2, Cognitive Toolkit, TensorFlow and others rely on GPU Accelerated Libraries such as CuDNN, TensorRT, and NCCL to deliver high performance multi-GPU accelerated training and inferencing.
The new Cisco UCS C480 ML M5 Rack Server: A 4U AI Compute Platform powered by 8 NVIDIA Tesla V100-32G GPUs with NVLink, the revolutionary high-speed interconnect, provides developers faster training cycles and improved scalability for multi-GPU system configurations.
This system is the latest addition to the existing Cisco portfolio of B-Series, C-Series, and HyperFlex systems with GPUs to address any AI/ML/DL use case – test/dev, training, and inferencing.