Will the cost of scaling infrastructure limit AI’s potential?

by | Jul 4, 2024 | Technology

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AI delivers innovation at a rate and pace the world has never experienced. However, there is a caveat, as the resources required to store and compute data in the age of AI could potentially exceed availability. 

The challenge of applying AI at scale is one that the industry has been grappling with in different ways for some time. As large language models (LLMs) have grown, so too have both the training and inference requirements at scale. Added to that are concerns about GPU AI accelerator availability as demand has outpaced expectations.

The race is now on to scale AI workloads while controlling infrastructure costs. Both conventional infrastructure providers and an emerging wave of alternative infrastructure providers are actively pursuing efforts to increase the performance of processing AI workloads while reducing costs, energy consumption, and the environmental impact to meet the rapidly growing needs of enterprises scaling AI workloads. 

“We see many complexities that will come with the scaling of AI,” Daniel Newman, CEO at The Futurum Group, told VentureBeat. “Some with more immediate effect and others that will likely have a substantial impact down the line.”

Countdown to VB Transform 2024

Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications int …

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We want to hear from you! Take our quick AI survey and share your insights on the current state of AI, how you’re implementing it, and what you expect to see in the future. Learn More

AI delivers innovation at a rate and pace the world has never experienced. However, there is a caveat, as the resources required to store and compute data in the age of AI could potentially exceed availability. 

The challenge of applying AI at scale is one that the industry has been grappling with in different ways for some time. As large language models (LLMs) have grown, so too have both the training and inference requirements at scale. Added to that are concerns about GPU AI accelerator availability as demand has outpaced expectations.

The race is now on to scale AI workloads while controlling infrastructure costs. Both conventional infrastructure providers and an emerging wave of alternative infrastructure providers are actively pursuing efforts to increase the performance of processing AI workloads while reducing costs, energy consumption, and the environmental impact to meet the rapidly growing needs of enterprises scaling AI workloads. 

“We see many complexities that will come with the scaling of AI,” Daniel Newman, CEO at The Futurum Group, told VentureBeat. “Some with more immediate effect and others that will likely have a substantial impact down the line.”

Countdown to VB Transform 2024

Join enterprise leaders in San Francisco from July 9 to 11 for our flagship AI event. Connect with peers, explore the opportunities and challenges of Generative AI, and learn how to integrate AI applications int …nnDiscussion:nn” ai_name=”RocketNews AI: ” start_sentence=”Can I tell you more about this article?” text_input_placeholder=”Type ‘Yes'”]

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