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.

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.

Delayed insights mean missed opportunities. With a Pure Storage-powered NVIDIA AI Data Platform, organizations can turn huge volumes of data into real-time intelligence. In today's fast-paced business world, milliseconds matter. The NVIDIA AI Data Platform, integrated with FlashBlade//EXA and Portworx, enables organizations to not just accelerate inference but also unlock precision reasoning at scale.

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.

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

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.

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.

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

High-performance storage and a comprehensive data strategy are both critical to maximizing the benefits of GenAI solutions, which can generate and consume massive amounts of data

In the face of increasing power demands brought by HPC services, the efficient use of whitespace and reliable energy distribution and management systems become top priorities.

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.

This article discusses the growing demand for high-performance computing (HPC) and artificial intelligence (AI) applications and how AMD is addressing these needs with their AMD Instinct MI300 Series accelerators and AMD CDNA 3 architecture.