Overview: GPUs provide the flexibility and computing power needed to train large AI models, while TPUs optimize tensor-heavy ...

Geekbench 7 is out now for Windows, macOS, Linux, Android, and iOS, and every Geekbench 6 CPU score you relied on for ...

For much of the generative AI boom, GPUs have dominated discussions around AI infrastructure as companies raced to build ...

The growing demand for server CPUs to handle agentic AI and inference workloads will be one of the biggest catalysts for AMD ...

The future of AI isn't just GPUs and NPUs. New CPUs from Arm, Intel, and AMD are bringing AI acceleration to next-gen phones and PCs.

Server processor market share for AMD has surged from 8% to 46%, driven by a savvy hybrid AI strategy, ROCm software, and ...

Nvidia has folded its PhysicsNeMo physics-AI libraries and a set of GPU math libraries into the NVIDIA Agent Toolkit, making ...

For the past decade, the artificial intelligence landscape followed a remarkably simple recipe: if you wanted better AI, you built larger models, fed them more data, and added more GPUs. The formula ...

Macworld explores Apple’s upcoming A20 Pro chip, expected to power the iPhone 18 Pro and iPhone Ultra as Apple’s first 2nm processor manufactured by TSMC. The chip promises significant performance ...

NVIDIA has released CUDA Toolkit 13.4 as a developer preview — and with it, the company has done something it has never done before: shipped an official, first-party CUDA development package that ...

It began with video games, a paintball experiment and a bold bet that few understood. Today, Nvidia has become a company every tech giant depends on to build the future of artificial intelligence.

The cards support the massive data bandwidth of sensor fusion, radar processing, electro-optical and infrared sensors, fire ...