Latest Marvell Blog Articles
-
August 04, 2026
Marvell Structera™ X, A and S: A Comprehensive CXL Portfolio Powering AI Memory Innovation
By Khurram Malik, AVP, Data Center Memory and Storage Solutions, Marvell
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.
Marvell 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—it 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, Marvell 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, Marvell is driving it toward higher bandwidth, deeper data optimization and increasingly disaggregated memory environments built to meet the growing demands of AI workloads.
-
August 04, 2026
Photonic Fabric™ Technology: How Optical Connectivity Enables the Next Generation of AI Infrastructure
By Ravi Mahatme, Senior Director, Product Management, Photonic Fabric Business Unit, Marvell
AI infrastructure is entering a new architectural era. As AI inference scales, performance increasingly depends not simply on adding more compute, but on how efficiently compute, memory and connectivity operate together as one unified AI infrastructure system. Modern AI inference workloads are insatiable consumers of memory. Large language models, reasoning models and agentic AI applications require rapid access to model parameters, embeddings and rapidly growing key-value (KV) caches that preserve conversational context. These working data sets are growing into the hundreds of gigabytes, and increasingly terabytes, making memory capacity, bandwidth and latency just as important as accelerator performance. As AI infrastructure scales, overall system performance increasingly depends on how efficiently accelerators can access, move and utilize memory resources rather than simply adding more compute.
Why AI Needs a New Memory Tier
Today's AI memory hierarchy was never designed for inference at the scale modern workloads demand. High bandwidth memory (HBM) attached directly to GPUs delivers exceptional performance but remains expensive and capacity constrained. System DRAM provides larger memory pools but cannot economically scale alongside every accelerator. NVMe SSDs offer abundant capacity, yet their latency makes them unsuitable for serving active inference workloads.
This challenge is especially visible in large language models, where growing KV caches must remain readily accessible to avoid repeatedly recomputing previous tokens. Keeping these caches entirely in HBM is prohibitively expensive, while moving them to storage introduces latency that reduces token generation performance. The result is that GPUs increasingly spend valuable cycles waiting for data rather than performing inference.
-
August 04, 2026
Powering the Next Generation of PCIe Gen6 NVMe SSDs with the Marvell® Bravera™ SC6 SSD Controller
By RC Camillo, Product Management Director, Custom Cloud Solutions, Marvell
As PCIe Gen6 storage becomes increasingly important for AI infrastructure, cloud platforms, enterprise databases, and hyperscale data centers, SSD controllers must deliver significantly more than raw bandwidth. They need intelligent flash management, enterprise-grade security, advanced error correction and scalable architecture.
The Marvell® Bravera™ SC6 SSD Controller (MV-SF1410) addresses these challenges by combining PCIe Gen6 connectivity, NVMe 2.2 compliance, support for high-speed NAND flash, powerful multi-core processing, integrated security and advanced reliability technologies into a single enterprise-ready platform. The Marvell® Bravera™ SC6 SSD Controller (MV-SF1410) is engineered to meet the demanding requirements of modern data centers and enterprise storage environments. Designed to support PCIe Gen6 NVMe SSDs, this advanced controller combines exceptional performance, scalability, robust security and intelligent flash management.
-
July 30, 2026
The Marvell Internship Experience: Voices from the Program
By Vienna Alexander, Marketing Content Professional, Marvell
Summer at Marvell means one thing: welcoming a new class of interns. The company has established a strong global internship program that has roughly doubled in size over the past few years. This year’s cohort includes more than 500 interns from over 100 universities and schools across 16 countries.
During the program, interns gain hands-on experience working in collaborative teams, contributing to the development of leading-edge semiconductor solutions at a Fortune 500 company. The internship provides opportunities to connect with fellow interns to build community while learning about Marvell products and businesses directly from executive leaders.
-
July 28, 2026
Performance, Connectivity and Programmability: How Teralynx T100 Sets a New Standard in AI Networking
By Rajagopal Krishnaswamy, Associate Vice President, Marvell
What do customers want from next-generation AI networks? Everything.
AI networks will need to manage exponentially more data across a rapidly expanding number of endpoints and evolving topologies, while reducing both cost and energy per bit. And, with data spending more than 30% of the time inside networks during training,1 congestion, transmission errors and other problems need to be kept to a minimum.
These networks will also need to be able to scale rapidly: AI networking spending is expected to reach $81.3 billion by 2030, more than 10x 2025 levels.2
Recent Posts
- Marvell Structera™ X, A and S: A Comprehensive CXL Portfolio Powering AI Memory Innovation
- Photonic Fabric™ Technology: How Optical Connectivity Enables the Next Generation of AI Infrastructure
- Powering the Next Generation of PCIe Gen6 NVMe SSDs with the Marvell® Bravera™ SC6 SSD Controller
- The Marvell Internship Experience: Voices from the Program
- Performance, Connectivity and Programmability: How Teralynx T100 Sets a New Standard in AI Networking
