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January 21, 2025

AI at Scale: A Special Report

By Kirt Zimmer, Head of Social Media Marketing, Marvell

AI at Scale

Marvell’s business is accelerated infrastructure for the AI era, which is a fast-evolving space that can occasionally confuse even the most earnest student. To help you keep up, we’ve partnered with VentureBeat to explore a multitude of content about that subject:

  • Build or buy? Scaling your enterprise GenAI pipeline in 2025
    Enterprise leaders are debating whether to buy AI tools, build their own, or some combination of the two. Companies like Wayfair and Expedia offer valuable insights for organizations looking to scale LLMs effectively.
  • Purpose-built AI hardware: Smart strategies for scaling infrastructure
    Custom AI hardware is the unsung hero of scalable AI infrastructure, helping to tackle a range of issues including performance, cost, and security. For enterprises looking to transition in this rapidly evolving landscape, there’s some great advice here.
  • AI factories are factories: Overcoming industrial challenges to commoditize AI
    Sixty years ago, Alabama was home to a 1.6GW coal fired power plant with the world's tallest chimney. That same site today houses a Google data center. The operations are obviously very different, but some of the infrastructure challenges are somewhat familiar. Read what AI 'factory' really means.
  • 4 bold AI predictions for 2025
    We’ve seen plenty of “predictions for the coming year” pieces in other publications that are honestly pure fluff, but this ain’t that. If your brain is activated by inference costs, reasoning models, transformer alternatives and LLM scaling laws, you’ll appreciate that even annual predictions can be smart and thought-provoking. 
  • The era of custom chips
    Marvell’s own Raghib Hussain wrote this one, and it perfectly illustrates why we’re so excited about our company’s position at the center of this AI revolution. It’s full of details like this: “Optimization can potentially increase memory inside the chip package by up to 33%, reduce interface power by 70% and increase the available silicon real estate for logic functions by close to 25%.” As Raghib puts it, “The best infrastructure will win.”
  • Edge computing’s rise will drive cloud consumption, not replace it
    Data traffic is fast becoming a challenge in AI and driving up cost and power. So how are organizations cutting bandwidth by 92% in image accuracy by better balancing workloads across the cloud and the edge?
  • Winning the war against adversarial AI needs to start with AI-native SOCs
    I was prepared to throw shade at the idea of this alleged “war,” but the label fits. You have the bad guys (adversarial AI), the good guys (AI-native Security Operation Centers), and some interesting strategies to consider for this digital battleground. Cloud intrusions have surged by 75% year-over-year, but AI-driven SOCs have reduced incident response times by up to 50%.
  • Not just hype – here are real-world use cases for AI agents
    AI agents are all the buzz right now, so when your uncle asks you what they are, just send him this article. And if you’re curious about different ways these agents might help your business, give it a read yourself.
  • The path forward for gen AI-powered code development in 2025
    Coding has changed the world, and now AI has transformed coding. Check out what AI-powered coding tools can do, can’t do (yet), and where actual humans fit into all this.

If you’re pinched for time, you could always ask AI to summarize each article. But you would miss so many intriguing details; this path is not advised. Even in this high-tech world, some things are best done the old-fashioned way.

 

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This blog contains forward-looking statements within the meaning of the federal securities laws that involve risks and uncertainties. Forward-looking statements include, without limitation, any statement that may predict, forecast, indicate or imply future events or achievements. Actual events or results may differ materially from those contemplated in this blog. Forward-looking statements are only predictions and are subject to risks, uncertainties and assumptions that are difficult to predict, including those described in the “Risk Factors” section of our Annual Reports on Form 10-K, Quarterly Reports on Form 10-Q and other documents filed by us from time to time with the SEC. Forward-looking statements speak only as of the date they are made. Readers are cautioned not to put undue reliance on forward-looking statements, and no person assumes any obligation to update or revise any such forward-looking statements, whether as a result of new information, future events or otherwise.

Tags: custom computing, ASIC, AI accelerator processing units, AI

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