Modern compute-heavy projects place demands on infrastructure that standard servers cannot satisfy. Artificial intelligence ...

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.

At its annual GTC conference for AI developers, Nvidia today announced its next-gen Hopper GPU architecture and the Hopper H100 GPU, as well as a new data center chip that combines the GPU with a high ...

It's CPU vs. GPU. The more immediate news for Intel was the announcement of its latest processor for machine learning. The Xeon Phi is getting an upgrade this year nicknamed Knights Landing. Yesterday ...

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 ...

Within a year or so, with the launch of the “Grace” Arm server CPUs, it will not be heresy for anyone at Nvidia to believe, or to say out loud, that not every workload in the datacenter needs to have ...

Why it matters: Watching the evolution of the computing industry over the last few years has been a fascinating exercise. After decades of focusing almost exclusively on one type of chip – the CPU – ...