This is a kind of scale-in performance that is enabled through better training algorithms and larger deep neural network datasets. But Nvidia has a scale-out play it is announcing as well. Nvidia is ...

GPU-based solutions are empowering more informed, real-time decisions: “With the need to embed more intelligence and automation into data analytics to address scientific and business challenges, ...

It's also worth noting that the leading deep learning frameworks all support Nvidia GPU technologies. Moreover, when using Tesla V100 GPUs, these are up to 3 times faster than using Pascal-based ...

Deep learning is a branch of machine learning that attempts to train computers to identify patterns and objects, in the same way humans do. For example, Google Brain, a deep learning research project ...

Nvidia has the added challenge of transparently upgrading its current deep learning customers to use Volta’s Tensor Core capability as new Volta-based GPU products enter the market. Tensor Cores gives ...

The graphics card offers up to nine times the deep learning performance of its predecessor, the Titan Xp, according to Nvidia. ’With Titan V, we are putting Volta into the hands of researchers ...

One Stop Systems introduced two new GPU accelerated servers at SC16 last month that can accommodate up to eight NVIDIA Tesla P100s in order to provide augmented performance for machine learning and ...

NVIDIA’s Hopper H100 Tensor Core GPU made its first benchmarking appearance earlier this year in MLPerf Inference 2.1. No one was surprised that the H100 and its predecessor, the A100, dominated ...

Powered by Nvidia Volta and its Tensor Core GPU architecture, the GPU Cloud launched in spring 2017. The Nvidia container registry supports popular deep learning tools like TensorFlow, Microsoft ...

For other general-purpose workloads, i.e. other than deep learning, the NVidia V100 is challenged from the performance and efficiency perspective. The re-configurability of FPGAs in addition to the ...