Marvell Blogs

Posts Tagged 'xPU utilization'

• October 14, 2026

  

  

  

  Scaling AI Inferencing with the Second Generation of Marvell Structera X: Industry-first Six-channel CXL 3.2 Memory Expanders

  By Arifur Rahman, Director of Product Marketing, Custom Cloud Solutions, Marvell

  AI and ML workloads are rewriting the rules of data center design. Memory demands are reaching an unprecedented level due to growing LLM sizes, expanding context windows and growing KV-cache requirements. Traditional servers are not architected to meet this surge. Memory has become the primary bottleneck constraining AI performance and infrastructure efficiency. CXL-based memory expansion has emerged as one of the definitive architectural answers, and Marvell is raising the bar.

  Building on the strong market adoption of the first-generation Structera® X CXL controllers, the second-generation Structera X 3406 and Structera X 3506, co-developed and co-architected with leading hyperscalers, address the most demanding AI infrastructure requirements.

  Modern AI inference workloads, from LLM serving to deep learning recommendation models (DLRM), are memory bound. KV-cache capacity determines time-to-first-token (TTFT), throughput and XPU utilization. When memory runs out, performance collapses.

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• December 16, 2025

  

  

  

  Custom Silicon: A Sea Change for Semiconductors

  By Sandeep Bharathi, president, Data Center Group, Marvell

  This blog was originally posted at Fortune.

  Semiconductors have transformed virtually every aspect of our lives. Now, the semiconductor industry is on the verge of a profound transformation itself.

  Customized silicon—chips uniquely tailored to meet the performance and power requirements of an individual customer for a particular use case—will increasingly become pervasive as data center operators and AI developers seek to harness the power of AI. Expanded educational opportunities, better decision making, ways to improve the sustainability of the planet all become possible if we get the computational infrastructure right.

  The turn to custom, in fact, is already underway. The number of GPUs—the merchant chips employed for AI training and inference—produced today is nearly double the number of custom XPUs built for the same tasks. By 2028, custom accelerators will likely pass GPUs in units shipped, with the gap expected to grow.¹ 

  

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• February 14, 2023

  

  

  

  The Three Things Next-Generation Data Centers Need from Networking

  By Amit Sanyal, Senior Director, Product Marketing, Marvell

  Data centers are arguably the most important buildings in the world. Virtually everything we do—from ordinary business transactions to keeping in touch with relatives and friends—is accomplished, or at least assisted, by racks of equipment in large, low-slung facilities.

  And whether they know it or not, your family and friends are causing data center operators to spend more money. But it’s for a good cause: it allows your family and friends (and you) to continue their voracious consumption, purchasing and sharing of every kind of content—via the cloud.

  Of course, it’s not only the personal habits of your family and friends that are causing operators to spend. The enterprise is equally responsible. They’re collecting data like never before, storing it in data lakes and applying analytics and machine learning tools—both to improve user experience, via recommendations, for example, and to process and analyze that data for economic gain. This is on top of the relentless, expanding adoption of cloud services.

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