Widely available GPU-accelerated servers, combined with better hardware and popular programming languages like Python and C/C++, along with frameworks such as PyTorch, TensorFlow and JAX, simplify the development of GPU-accelerated ML applications. These applications serve diverse purposes, from medical research to self-driving vehicles, relying on large datasets and GPU clusters for training deep neural networks. Inference frameworks apply knowledge from trained models to new data with optimized clusters for performance. The learning cycles involved in AI workloads can take days or weeks, and high-latency communication between server clusters can significantly impact completion times or result in failure. AI workloads demand low-latency, lossless networks, requiring appropriate hardware, software features, and configurations. This article will explain advanced queueing solutions used by all the major OEMs in the Network Operating Systems (NOS) that support ECN and PFC.

Intel's Advanced Matrix Extensions (AMX) in 5th-gen Xeon processors revolutionize AI computing by enabling efficient matrix operations directly on CPUs. Supporting models with up to 13 billion parameters, AMX offers a cost-effective and power-efficient alternative to GPUs, simplifying AI deployment and democratizing access to high-performance AI capabilities across diverse sectors.

Innovation today demands more than just visionary ideas; it requires robust infrastructure, especially for AI. Intel's latest processors offer scalable, efficient solutions to overcome IT challenges, enabling organizations to implement strategic initiatives effectively. Discover how Intel and WWT can help your business achieve its AI-driven goals.

AMD and OpenAI's strategic partnership will power next-gen AI infrastructure with 6 gigawatts of AMD Instinct GPUs, starting with MI450 in 2026. This collaboration accelerates AI advancements, aligning strategic interests and creating significant shareholder value, while enabling large-scale AI deployments and advancing the entire ecosystem.

From digital twins to sustainable IT, these are eight trends our WWT data solutions experts are keeping their eyes on for 2023 and beyond.

As the world becomes more complex, high-performance computers with AI give scientists, engineers and business technologists a powerful way to tackle some of humanity's most challenging problems.

Read about the exciting announcements Dr. Lisa Su, AMD executives, and industry luminaries unveiled at AMD's Advancing AI 2025 Keynote event

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.

Learn about the sustainability and efficiency benefits that high-performance computing can provide for the pharmaceutical, energy, manufacturing and shipping industries as part of their environmental, social and corporate governance initiatives.

Not all storage solutions are created equal when it comes to supporting the demands of AI workloads. Learn about WWT's top considerations for designing, deploying and implementing high-performance storage (HPS) solutions.

Servers based on AMD EPYC 9005 Series processors offer leadership performance, density and efficiency to support the most demanding data center initiatives today: everything from corporate AI enablement and large-scale hybrid cloud buildouts to business-critical enterprise applications.

Read this article from Schneider Electric to learn from experts about the demand for AI computing to train LLMs and the importance of robust IT infrastructure to support AI applications.