Intel's Gaudi 3 AI accelerator offers a powerful, energy-efficient solution for generative AI, providing flexibility, scalability, and cost savings. This innovative technology supports diverse AI workloads and works with open standards based-systems, enabling organizations to maximize ROI and future-proof their AI infrastructure.

Discover the benefits of GPU-as-a-Service solutions or GPU Clouds, and how they can assist organizations in meeting diverse AI workload demands through a scalable, consumption-based service model.

The AI Proving Ground enables organizations to quickly, confidently and safely develop transformational AI solutions that deliver real business results in a fraction of the time.

Learn about the components of high-performance networking, including available transport technologies and the secret to making things go fast.

Organizations deploying modern, purpose-built infrastructure with the right mix of central processing units (CPUs), accelerators, and software based on specific workload requirements are reaping better business outcomes at a lower total cost of ownership (TCO).

AI is transforming data center facilities. While traditional data centers support lower power densities, AI workloads demand significantly higher power per rack, necessitating infrastructure adaptations to meet these challenges.

AI workloads require significant data and computational power, with billions of parameters and complex matrix operations. Inter-network communication accounts for a significant portion of job completion time. Traditional network architectures are insufficient for large-scale AI training, necessitating investments in new network designs. Arista Networks offers high-bandwidth, low-latency and scalable connectivity for GPU servers, with features like Data Center Quantized Congestion Notification and intelligent load balancing. Arista's AI Leafs and Spines provide high-density and high-performance switches for AI networking. Different network designs are recommended based on the size of the AI application. A dedicated storage network is recommended to handle the large datasets used in AI training. Arista's Cloud Vision Portal and AI Analyzer tools provide automated provisioning and deep flow analysis. Arista's IP/Ethernet switches are well-suited for AI/ML workloads, offering energy-efficient interconnects and simplified network management.

Learn how WWT and Dell Technologies are partnering to help our clients overcome the barriers to enterprise AI adoption through purpose-built HPA.

HPE Cray XD combines supercomputing power with enterprise agility, creating a "framemain" approach—offering mainframe-like integration while maintaining openness, allowing organizations to build scalable AI infrastructure without ecosystem lock-in.

Generative AI is evolving, shifting from raw performance to balancing efficiency, cost, and security. As AI matures, organizations must optimize infrastructure and prioritize power efficiency to reduce costs and risks. Adapting to these changes ensures a competitive edge and fosters continued innovation in the rapidly advancing AI landscape.

An in-depth guide for IT decision makers navigating the complex IT landscape.

Data center readiness refers to the preparedness of a facility to handle the unique demands posed by AI workloads. As AI applications become more prevalent, understanding the impact on power, cooling, physical infrastructure and networking is crucial.