From the origins of big data to the development of deep learning and the era of large language models (LLMs), data processing applications have become increasingly capable and influential. Each breakthrough has redefined what’s possible and presents unique challenges and opportunities that datacom engineers must navigate to ensure that operations remain robust, scalable and efficient.
Data centers represent the physical infrastructure that enables our escalating digital economy so their reliability is imperative. However, the most recent advancements in AI and ML are placing unprecedented demands on their current capabilities. To efficiently store and process these vast datasets requires greater bandwidths and lower latencies.
Additionally, as AI models grow in complexity, the compute bandwidth required for training and inference can skyrocket, contributing to higher operational costs as well as the need to adopt more advanced thermal and power management strategies. In fact, training large generative AI models like ChatGPT 4.0 can require up to one septillion (1025) FLOPS-worth of processing, costing an estimated $100 million. This impact will be further felt as ML applications move further to the edge to support applications like autonomous vehicles, medical diagnostics and smart homes — a market that is expected to exceed $22 billion USD by 2034.
This calls for a new generation of more powerful and efficient GPU hardware, greater data transfer rates inside networks and scalable systems that can adapt to the growing demands of AI and ML.
Molex is at the forefront of these developments, working alongside our customers to co-develop innovative solutions ranging from our first-to-market 224G product portfolio to the next generation of PCIe technologies. Our global, interdisciplinary team of engineering experts is focused on efficiency, providing scalable and modular interconnect solutions that address speed, responsiveness, signal integrity, thermal management, space constraints and ease of maintenance.
