Artificial Intelligence (AI) has emerged as a transformative technology, revolutionizing various industries and numerous aspects of daily life, from healthcare and financial services to entertainment. The swift evolution of real-time gaming, virtual reality, generative AI, and metaverse applications is reshaping the interactions between network, compute, memory, storage, and interconnect I/O. As AI continues its rapid advancement, networks must adapt to the immense growth in traffic that traverses hundreds and thousands of processors, handling trillions of transactions and gigabits of throughput.

As AI transitions from laboratory research to mainstream adoption, there is a significant demand for increased network and computing resources. Recent technological developments are only the foundational elements of what is anticipated in the next decade. It is expected that AI clusters will expand substantially in the coming years. A common characteristic of these AI workloads is their intense data and computational demands.

A typical AI training workload involves billions of parameters and large sparse matrix computations distributed across hundreds or thousands of processors, including CPUs, GPUs, or TPUs. These processors engage in intensive computations and then exchange data with their peers. Data from these peers is either reduced or merged with local data before another processing cycle begins. In this compute-exchange-reduce cycle, approximately 20-50% of the job time is spent on communication across the network. Consequently, any bottlenecks in the network can significantly impact job completion times.

As AI technology continues to grow, the infrastructure supporting it must also evolve. Ensuring efficient data exchange and minimizing network bottlenecks are crucial to optimizing AI workloads. The increased network demands necessitate advancements in network architecture to support the high throughput and low latency required by AI applications.

This white paper explores the current state and future trends of AI technology, particularly focusing on the infrastructure needed to support its growth. It discusses the challenges faced by networks in handling the colossal growth in AI-related traffic and the solutions required to overcome these challenges. By examining the compute-exchange-reduce cycle in detail, the paper highlights the importance of efficient network communication in reducing job completion times and improving overall AI performance. The insights provided will be valuable for stakeholders looking to adapt their network infrastructures to meet the evolving demands of AI technology.

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