Posts Tagged 'AI'
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June 24, 2026
Structera X and A CXL Compression: Making Every Gigabyte Count
By Arifur Rahman, Director of Product Marketing, Custom Cloud Solutions, Marvell
Modern AI workloads are insatiable consumers of memory. Deep learning recommendation models (DLRM), large language model (LLM) inference, in-memory databases and vector search engines all share a common bottleneck: there is never enough DRAM, and what exists is very expensive.
At today's spot prices—$27–$37 per GB for server-grade DDR5 RDIMMs1—a 12TB memory pool requires nearly half a million dollars in DRAM alone. Meanwhile, AI infrastructure buildouts are consuming server DRAM capacity faster than fabs can produce it, driving prices up 300–400% since mid-2025.1, 2
CXL memory expansion was supposed to solve this. And it does—but there's a subtler lever that most solutions ignore: the data sitting in that memory is compressible, and most CXL controllers don't touch it.
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June 17, 2026
Plasmonics: A Path to Higher Bandwidth in Optics in the AI Era
By Claudia Hoessbacher, Senior Director, and Wolfgang Heni, Director, Optical Engineering, Marvell
Plasmons have been used to accelerate drug discovery, enhance the sensitivity of sensors and even create artistic treasures in the Roman era.
Ongoing research at Marvell seeks to harness them to improve the performance of optical networks for the AI era. Plasmonics, a technology that leverages the properties of surface plasmon polaritons (SPPs), provides a promising pathway for enhancing the roadmap of silicon photonic (SiPho) light engines, a critical component inside optical modules.
Plasmonic-based SiPho light engines could support modules operating at 3.2T and beyond while consuming a fraction of the space and power per bit of modules based on existing technologies. Manufacturers could leverage foundry process technologies for scaling production.
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May 28, 2026
Open CPX Sets the Stage for More Flexible, Scalable Connectivity
By George Hervey, Associate Vice President, Cloud Switch Marketing, Marvell
Co-packaged connectivity is coming. The Open CPX MSA (Co-packaging Multisource Agreement) is working to simplify adoption.
The consortium, which includes Marvell and other leaders in connectivity, is developing specifications and standards for solutions for integrating near-packaged optical (NPO) and/or co-packaged optical (CPO) technology into switches and servers in scalable, repeatable ways. Members are also working to support interoperability with co-packaged copper (CPC).
The idea is to give data center service providers, equipment manufacturers and others a unified framework for next-generation connectivity to accelerate innovation and meet the surging demand for these technologies. Fewer than one million near- and co-packaged ports shipped in 2025, according to LightCounting; by 2030, shipments are projected to surpass 100 million ports per year.1 Standards that can ensure predictability and flexibility will be critical in enabling this expected growth.
“The initial target of the MSA will be to develop an optimized optical engine with a defined pluggable socket and electrical connector system supporting high speed and high-density connectivity between a switch or processor and co-packaged and near-package interconnects,” the Open CPX MSA website states. “The specifications will define connector mechanicals, thermals, electrical pinout, mechanical form factors, electrical, optical, and management interface specifications to ensure interoperability between multiple vendors of Open CPX.”
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May 19, 2026
Why Scale-up AI Networks Demand Scalable Optical Test
By Andrew Yick, Technical Associate Vice President, Operations Engineering, Marvell
This article was first published in Photonic Integrated Circuits magazine.
The dominant challenge in modern AI infrastructure is not just the performance of a single accelerator but scaling up to thousands of accelerators (XPUs) in a cluster. Training and inference workloads now depend on an interconnect that can stitch these accelerators into a high-bandwidth, low-latency system, where performance is governed as much by the network as by the compute itself.
As these systems scale, physics asserts itself. Electrical links over copper hit a practical ceiling as routing density and channel loss collide, turning the loss bandwidth product into an impassable constraint. The choice is binary: either move electrical-to-optical conversion closer to the Application-Specific Integrated Circuit (ASIC) or surrender the link budget. Thus, to bypass this electrical wall, optics must migrate from the board edge and onto the ASIC package.
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May 06, 2026
Scale-up Network Solutions for AI Infrastructure
By Preet Virk, Senior Vice President and General Manager, Photonic Fabric Business Unit
Modern AI infrastructure is built around multi-rack systems where thousands to tens of thousands of accelerators operate as a single logical compute element. As agentic AI and Mixture of Experts (MoE) models accelerate AI adoption, they are driving unprecedented scale and communication demands across data center infrastructure. These systems are connected by scale-up and scale-out networks that must deliver high bandwidth, low latency and efficient power. As these networks extend across racks, maintaining that performance becomes a primary challenge.
As AI systems grow in complexity and scale, the network becomes the backbone of the compute system. Large-scale clusters require massive XPU-to-XPU communication, driving an evolution beyond legacy protocols like PCIe® to encompass UALink™ (Ultra Accelerator Link), ESUN (Ethernet scale-up networking) and NVLink.
Meeting these requirements demands a new approach to connectivity. Marvell provides a comprehensive AI connectivity portfolio spanning scale-up, scale-out, scale-across and DCI (data center interconnect) network architectures. For scale-up networking, Marvell delivers copper and optical interconnects connecting XPUs, switches and memory. Within the rack, Marvell copper solutions provide low-latency, power-efficient short-reach connectivity, while Marvell optical interconnects enable high-performance scaling beyond the rack. This enables XPUs to operate as a more efficient, unified system as scale-up domains expand.
