Five AI Compute Architectures Every Engineer Should Know: CPUs, GPUs, TPUs, NPUs, and LPUs Compared

The idea of asking homeowners to host boxes full of GPUs is a symptom of the woeful dearth of data center space needed for AI computing.

Silicon photonics is emerging as a way move massive amounts of data among GPUs and CPUs in HPC systems, but what if you could compute purely with light and photonics? […] The post Lumai’s Photonic Chip Harnesses Light for Big AI Compute Speedup appeared first on AIwire.

This Spring Astronomy Day, here’s a look at how AI and GPUs are helping astronomers work through unprecedented volumes of cosmic data.

Learn how Google’s TPUs power increasingly demanding AI workloads with this new video.

Google's newest TPUs are faster and cheaper than the previous versions. But the company is still embracing Nvidia in its cloud — for now.

Google's new generation of Tensor AI chips is actually two chips, one for inference and one for training.

In the race to incorporate AI into many industries, GPUs have become one of the most sought-after resources in enterprise computing. They are expensive, hard to find, and increasingly seen […] The post Companies Are Racing to Buy GPUs. Many Sit Idle appeared first on AIwire.

In an age of constrained compute, learn how to optimize GPU efficiency through understanding architecture, bottlenecks, and fixes ranging from simple PyTorch commands to custom kernels. The post A Guide to Understanding GPUs and Maximizing GPU Utilization appeared first on Towards Data Science.