Graphics Processing Unit (GPU)

A new cloud-computing company promises to provide scarce AI processing power to startups and researchers. Voltage Park, a nonprofit north of Silicon Valley, will offer processing power from 24,000 top-of-the-line Nvidia H100 graphics processing units (GPUs)...

An upstart supplier of AI chips secured a major customer. Cerebras, which competes with Nvidia in hardware for training large models, signed a $100 million contract with Abu Dhabi tech conglomerate G42. The deal is the first part of a multi-stage plan to build a network of supercomputers.

Nvidia’s top-of-the-line chips are in high demand and short supply. There aren’t enough H100 graphics processing units (GPUs) to meet the crush of demand brought on by the vogue for generative AI, VentureBeat reported.

Chinese companies have found loopholes to sidestep United States limits on AI chips. Facing severe limits on U.S. exports of high-performance chips, Chinese AI firms are purchasing them through subsidiaries and using them through cloud services, the Financial Times reported.

Advanced AI requires advanced hardware. What if the global supply of high-end AI chips dries up? Most of the world’s advanced AI processors are manufactured in Taiwan, where tension with mainland China is rising.

New U.S. restrictions on chip sales aim to hamper China’s AI efforts. The U.S. government published sweeping limits on sales of processors that involve U.S. designs and technology to Chinese businesses. U.S. officials stated that the restrictions are meant to prevent China from militarizing AI.

Nvidia, known for chips designed to process AI systems, is providing access to large language models. Nvidia announced early access to NeMo LLM and BioNeMo, cloud-computing services that enable developers to generate text and biological sequences respectively.

The ban impacts Nvidia's A100 and H100 chips, and AMD's MI250.

Let’s get this out of the way: A brain is not a cluster of graphics processing units, and if it were, it would run software far more complex than the typical artificial neural network. Yet neural networks were inspired by the brain’s architecture.

The trend toward ever-larger models crossed the threshold from immense to ginormous. Google kicked off 2021 with Switch Transformer, the first published work to exceed a trillion parameters, weighing in at 1.6 trillion.

Google designed its own AI chip for its new smartphone — a snub to Qualcomm, the dominant chip vendor in Android phones. What’s new: Google debuted the Tensor chip last week

A major corporate acquisition could reshape the hardware that makes AI tick.What’s new: U.S. processor giant Nvidia, the world’s leading vendor of the graphics processing units (GPUs) that perform calculations for deep learning, struck a deal to purchase UK chip designer Arm for $40 billion.

A high-profile semiconductor startup made a bid for the future of AI computation. UK startup Graphcore released the Colossus Mk2, a processor intended to perform the matrix math calculations at the heart of deep learning more efficiently than other specialized processors.

Chips specially designed for AI are becoming much faster at training neural networks, judging from recent trials. MLPerf, an organization that’s developing standards for hardware performance in machine learning tasks, released results from its third benchmark competition.

The latest update of the acclaimed real-time object detector You Only Look Once is more accurate than ever. Researchers at Taiwan’s Institute of Information Science Academia Sinica offer YOLOv4 — the first version not to include the architecture’s original creators.