A GPU, or graphics processing unit, is a processor built to perform many calculations in parallel. Although originally designed for rendering graphics, this structure is well suited to the matrix and vector operations at the center of deep learning. GPUs accelerate both model training and inference, and multiple units can be connected to handle larger models or datasets. Performance depends not only on raw arithmetic speed but also on memory capacity, memory bandwidth, interconnects, numerical formats, cooling, and software libraries. Consumer, data-center, and integrated GPUs serve different workloads, so the most expensive chip is not automatically the most efficient choice for every AI application.
Amazon and Cerebras have partnered to combine their AI chips in a new AWS service designed to accelerate inference for chatbots, coding tools, and other generative AI applications.
Palantir and Nvidia unveiled a sovereign AI OS reference architecture designed to deliver turnkey AI data centers. The platform integrates Nvidia Blackwell systems with Palantir’s enterprise AI software stack.
Meta introduced four new in-house MTIA chips designed for AI training and inference as the company accelerates data center expansion. The chips aim to improve performance and reduce reliance on external hardware suppliers.
Nvidia and Nebius have formed a strategic partnership to build hyperscale AI cloud infrastructure, with Nvidia investing $2 billion to support gigawatt-scale AI computing capacity.
Honor confirmed its Robot Phone will launch in the second half of the year, featuring a 200MP camera with a built-in three-axis mechanical gimbal system.
Burger King is testing an AI-powered headset system called Patty, designed to monitor operations, assist employees, and track service patterns in real time.
Nvidia reports record Q4 revenue of $68.1B and net income of $43B, driven by strong AI data center demand and continued hyperscale investment.
Anthropic unveils a revised Responsible Scaling Policy with a Frontier Safety Roadmap, regular Risk Reports, and clearer separation between company commitments and industry recommendations.
ŌURA unveils a proprietary large language model for women’s health, combining clinical research and biometric data to deliver personalized, privacy-first AI guidance through Oura Advisor.
Meta plans to buy up to $100 billion in AMD GPUs and CPUs, issuing performance-based warrants as it ramps AI data centers and diversifies compute infrastructure.