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.

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.

Our storage experts highlight the eight most impactful trends they see shaping how businesses approach their storage infrastructure.

Learn about common myths for HPC and AI, and why these are inaccurate.

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

Learn how Granulate, an Intel company, can help overcome four challenges that companies face when it comes to optimizing Big Data workloads.

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.

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.

As the technology needs of businesses evolve, it's increasingly important for IT organizations to evolve with it.

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.