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Posts Tagged 'AI infrastructure'

• September 15, 2026

  

  

  

  Breaking Through the KV Cache Memory Wall with DPU-powered Network Storage

  By Chander Chadha, Director of Product Marketing, Storage Products, ³Ô¹ÏºÚÁÏ

  As AI models grow larger and inference workloads scale, larger models, longer context windows and growing KV caches are driving demands on memory resources. Traditional CPU-centric networked JBOF (Just Bunch of Flash) can¡¯t keep pace with the throughput, latency, and efficiency requirements of these modern AI clusters.

  DPU (data processing unit) -based storage is a compelling alternative to traditional storage, acting as the broker between the network and SSD for remote storage. By offloading storage, networking and security processing from the host CPU onto a dedicated DPU, AI infrastructure can move data closer to compute, reduce latency, and free up valuable CPU cycles for AI workloads.

  To address this need, ³Ô¹ÏºÚÁÏ offers the OCTEON DPU family, purpose-built for hyperscale cloud workloads and data center applications, extending its use specifically into network storage acceleration for AI environments.

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• September 10, 2026

  

  

  

  ³Ô¹ÏºÚÁÏ Demonstrates Scalable CXL Memory Infrastructure with Intel

  By Khurram Malik, Associate Vice President, Custom Cloud Solutions, ³Ô¹ÏºÚÁÏ

  

  The rapid growth of AI is creating unprecedented demand for memory capacity. As models get larger and workloads become more data-intensive, traditional server architectures are increasingly challenged to scale memory efficiently.

  At Flash Memory Summit (FMS), ³Ô¹ÏºÚÁÏ demonstrated how CXL can enable a more flexible and scalable approach to memory infrastructure, showcasing an end-to-end architecture at the ? booth.

  The demonstration combined an Intel platform with three ³Ô¹ÏºÚÁÏ? technologies: Structera? X CXL memory expander, Structera? S CXL switch and Alaska? P PCIe retimer. Together, they demonstrated how memory expansion, CXL switching and high-speed PCIe connectivity can work together within a real server platform.

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• September 09, 2026

  

  

  

  ³Ô¹ÏºÚÁÏ Structera X Extends CXL Interoperability to NVIDIA Vera

  By Arifur Rahman, Director of Product Marketing, Custom Cloud Solutions, ³Ô¹ÏºÚÁÏ

  Memory has become the defining constraint of modern AI infrastructure. Large language models, in-memory databases, and deep learning recommendation models all share the same bottleneck: there is never enough DRAM. Compute Express Link (CXL) was designed to break that bottleneck but a CXL memory expander is only as valuable as the breadth of platforms it can run on.

  That is why ecosystem enablement is a core pillar of ³Ô¹ÏºÚÁÏ's CXL strategy. Today we are marking a new milestone: successful interoperability of the ³Ô¹ÏºÚÁÏ? Structera? X CXL memory-expansion controller with the .

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• August 05, 2026

  

  

  

  Accelerating AI Infrastructure with ³Ô¹ÏºÚÁÏ Structera A and SK hynix CXL Memory: Enabling Efficient Near-Memory Processing

  By Khurram Malik, Associate Vice President, Custom Cloud Solutions, ³Ô¹ÏºÚÁÏ, and Kangkyu Park, Vice President, System Architecture, SK hynix

  The rapid growth of AI workloads is creating unprecedented demands on data center architectures. Modern AI applications, including large language models (LLMs), generative AI, recommendation systems, and high-performance computing, require significantly higher memory capacity, bandwidth, and efficiency.

  Traditional compute-centric architectures are increasingly limited by the movement of data between processors and memory. As AI models continue to scale, excessive data movement creates performance bottlenecks, increases latency, and drives higher power consumption.

  To address these challenges, the industry is moving toward memory-centric computing architectures enabled by Compute Express Link? (CXL?). CXL enables flexible memory expansion, memory pooling, and new system architectures that allow compute resources to operate more efficiently.

  ³Ô¹ÏºÚÁÏ and SK hynix are collaborating to enable the next generation of memory-centric computing by combining ³Ô¹ÏºÚÁÏ? Structera? A CXL-based near-memory acceleration technology with . Together, ³Ô¹ÏºÚÁÏ and SK hynix are helping accelerate the adoption of CXL-enabled architectures for AI data centers by delivering a highly efficient and scalable approach to memory processing.

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• August 04, 2026

  

  

  

  ³Ô¹ÏºÚÁÏ Structera? X, A and S: A Comprehensive CXL Portfolio Powering AI Memory Innovation

  By Khurram Malik, AVP, Data Center Memory and Storage Solutions, ³Ô¹ÏºÚÁÏ

  CXL has become one of the most important technologies shaping AI infrastructure. As hyperscalers race to deploy larger AI models, longer context windows and increasingly memory-intensive inference workloads, memory capacity and bandwidth have emerged as critical constraints on performance, efficiency and scaling. At the same time, CXL adoption is reaching an inflection point, moving from evaluation into real-world deployment across hyperscale environments.

  ³Ô¹ÏºÚÁÏ is leading this transition with Structera? X memory expansion solutions developed alongside the world¡¯s leading hyperscalers. The story begins with the shipping of Structera X 2404 and 2504 platforms, which have enabled hyperscalers to expand memory resources more efficiently, including extending the useful life of existing DDR4 investments while powering demanding AI workloads.

  Structera X is not a series of disconnected product eras¡ªit is a single, continuous architectural evolution. Today¡¯s generation is already delivering real hyperscaler deployments, ecosystem maturity and a compelling TCO advantage. From that foundation, ³Ô¹ÏºÚÁÏ is extending the architecture toward the next phase of AI infrastructure innovation, adding capabilities enabled by the evolving CXL 3.2 ecosystem, PCIe Gen 6 connectivity and more advanced multi-host memory sharing architectures. These advancements will create larger, more flexible memory pools, enabling more efficient sharing of resources across servers and improving infrastructure utilization at hyperscale. As the architecture advances, ³Ô¹ÏºÚÁÏ is driving it toward higher bandwidth, deeper data optimization and increasingly disaggregated memory environments built to meet the growing demands of AI workloads.

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• ³Ô¹ÏºÚÁÏ Demonstrates Scalable CXL Memory Infrastructure with Intel
• ³Ô¹ÏºÚÁÏ Structera X Extends CXL Interoperability to NVIDIA Vera
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