Hardware requirements vary for machine learning and other compute-intensive workloads. Get to know these GPU specs and Nvidia GPU models. Chip manufacturers are producing a steady stream of new GPUs.

Presenting you with a multi-tasking, all-in-one GPU, NVIDIA RTX 3090. So starting from Tensor cores to some awesome features like real-time ray facing, this GPU has it all. Solving research and data ...

What if the key to unlocking faster, more efficient machine learning workflows lies not in your algorithms but in the hardware powering them? In the world of GPUs, where raw computational power meets ...

Databricks, corporate provider of support and development for the Apache Spark in-memory big data project, has spiced up its cloud-based implementation of Apache Spark with two additions that top IT’s ...

H2O.ai, today announced that it has collaborated with NVIDIA to offer its best-of-breed machine learning algorithms in a newly minted GPU edition. In addition, H2O’s platform will be optimized for ...

In modern CPU device operation, 80% to 90% of energy consumption and timing delays are caused by the movement of data between the CPU and off-chip memory. To alleviate this performance concern, ...

Adobe, Baidu, Netflix, Yandex. Some of the biggest names in social media and cloud computing use NVIDIA CUDA-based GPU accelerators to provide seemingly magical search, intelligent image analysis and ...

In collaboration with the Metal engineering team at Apple, PyTorch today announced that its open source machine learning framework will soon support GPU-accelerated model training on Apple silicon ...