DIGITS 4 release candidate will be available this week as a free download for members of the NVIDIA developer program.

NVIDIA cuDNN provides high-performance building blocks for deep learning used by all leading deep learning frameworks. Version 5.1 delivers accelerated training of deep neural networks, like University of Oxford’s VGG and Microsoft’s ResNet, which won the 2016 ImageNet challenge.

Each new version of cuDNN has delivered performance improvements over the previous version, accelerating the latest advances in deep learning neural networks and machine learning algorithms.

cuDNN 5.1 release candidate is available today as a free download for members of the NVIDIA developer program.

The GPU Inference Engine (GIE) is a high-performance deep learning inference solution for production environments. GIE optimizes trained deep neural networks for efficient runtime performance, delivering up to 16x better performance per watt on an NVIDIA Tesla M4 GPU vs. the CPU-only systems commonly used for inference today.

The amount of time and power it takes to complete inference tasks are two of the most important considerations for deployed deep learning applications. They determine both the quality of the user experience and the cost of deploying the application.

Using GIE, cloud service providers can more efficiently process images, video and other data in their hyperscale data center production environments with high throughput. Automotive manufacturers and embedded solutions providers can deploy powerful neural network models with high performance in their low-power platforms.

The NVIDIA Deep Learning platform is part of the broader NVIDIA SDK, which unites into a single program the most important technologies in computing today—artificial intelligence, virtual reality and parallel computing.

Tesla P100 GPU accelerator. To meet the unprecedented computational demands placed on modern data centers, NVIDIA also has introduced the NVIDIA Tesla P100 GPU accelerator for PCIe servers, which delivers massive leaps in performance and value compared with CPU-based systems.

The Tesla P100 GPU accelerator for PCIe enables the creation of “super nodes” that provide the throughput of more than 32 commodity CPU-based nodes and deliver up to 70% lower capital and operational costs.

The Tesla P100 for PCIe is available in a standard PCIe form factor and is compatible with today's GPU-accelerated servers. It is optimized to power the most computationally intensive AI and HPC data center applications. A single Tesla P100-powered server delivers higher performance than 50 CPU-only server nodes when running the AMBER molecular dynamics code, and is faster than 32 CPU-only nodes when running the VASP material science application.

Later this year, Tesla P100 accelerators for PCIe will power an upgraded version of Europe’s fastest supercomputer, the Piz Daint system at the Swiss National Supercomputing Center in Lugano, Switzerland.