AI's Future Hinges on Network Advancements

Published Date: 1/08/2024

Traditional cloud infrastructure success is driven by being cost-effective, flexible, and scalable, which are also essential attributes for AI infrastructure.

The advent of Artificial Intelligence (AI) is reshaping various industries, driving innovation and efficiencies to unprecedented levels. However, similar to any disruptive technology introduction, AI will come with unique challenges and opportunities. AI infrastructure challenges lie in cost-effectively scaling storage, compute, and network infrastructure, while also addressing massive increases in energy consumption and long-term sustainability.

Intra data center networks for AI play a crucial role in hosting the traditional cloud services we use daily. Traditional cloud infrastructure success is driven by being cost-effective, flexible, and scalable, which are also essential attributes for AI infrastructure. However, a new and more extensive range of network performance requirements are needed for AI.

AI applications, such as Large Language Models (LLM) training leveraging Deep Learning (DL) and artificial neural networks, involve moving massive amounts of data within a data center over short, high-bandwidth, low-latency networks operating at 400Gb/s and 800Gb/s to 1.6Tb/s and higher in the future.

The distances within and between data centers will require different network solutions. AI campus networks will need to be located near available energy that is reliable, sustainable, and cost-effective. Campus data centers will be connected to each other and to distant data centers using optics optimized for specific cost, power, bandwidth, latency, and distances.

As AI infrastructure is hosted in new and existing data centers, interconnecting them will be required like they are interconnected today for traditional cloud services. This will be achieved using similar optical transport solutions albeit at higher rates.For enterprises, AI will drive an increasing need to migrate data and applications to the cloud due to economics, in-house gaps in AI expertise, as well as challenging power and space limitations. Once an LLM is properly trained, it will be optimized and “pruned” to provide an acceptable inferencing accuracy within a much smaller footprint in terms of compute, storage, and energy requirements.

Placing AI storage and compute assets in geographically distributed data centers closer to where AI is created and consumed, whether by humans or machines, allows for faster data processing for near real-time AI inferencing to be achieved. This means more edge data centers to interconnect.

Balancing electrical power consumption and sustainability is critical. AI is progressing at an increasingly rapid pace, creating new opportunities and challenges to address. Although AI infrastructure compute and storage consumes far more electrical energy than the networks that interconnect them, network bandwidth growth cannot scale linearly with associated power consumption – this is not sustainable or cost-effective.\n\nAI data is only valuable if it can move securely, sustainably, and cost-effectively from inside core data centers hosting AI LLM training to edge data centers hosting AI inferencing.

FAQS:

Q: What are the challenges of AI infrastructure?

A: The challenges of AI infrastructure lie in cost-effectively scaling storage, compute, and network infrastructure, while also addressing massive increases in energy consumption and long-term sustainability.

Q: What is the role of intra data center networks in AI?

A: Intra data center networks play a crucial role in hosting traditional cloud services and AI infrastructure, requiring cost-effective, flexible, and scalable network performance.

Q: How will AI impact data center interconnection?

A: AI will drive the need for more dynamic and higher speed bandwidth interconnections, requiring more cloud exchange infrastructure, which represents a new telco revenue-generating opportunity.

Q: What is the importance of edge data centers in AI?

A: Edge data centers will be critical in providing faster data processing for near real-time AI inferencing, allowing for more decentralized and efficient AI applications.

Q: How can AI infrastructure balance power consumption and sustainability?

A: AI infrastructure must balance power consumption and sustainability by reducing electrical power per bit, using more energy-efficient technologies, and adopting sustainable practices in data center design and operation.

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