Marvell Bets on Custom Silicon Future with 2nm SRAM Breakthrough
In a major stride toward redefining the future of AI and cloud infrastructure, Marvell Technology has introduced the industry’s first 2nm custom Static Random Access Memory (SRAM)—a compact powerhouse built to meet the growing memory demands of custom XPUs and AI accelerators powering hyperscale data centers.
The new SRAM is part of Marvell’s broader push to enhance memory hierarchy performance through customized silicon—an increasingly necessary move in a post-Moore’s Law era. As traditional transistor scaling hits physical and economic walls, chipmakers are looking to reclaim performance gains through tailored architectures. Marvell’s latest offering aims to do just that—efficiently and scalably.
Why It Matters
Memory is a notorious bottleneck in AI infrastructure. With workloads ballooning and the hunger for high-speed, high-capacity memory mounting, every byte—and every watt—counts.
Marvell’s 2nm custom SRAM delivers up to 6 gigabits of high-speed memory while cutting power usage by up to 66% and shrinking die area by up to 15% compared to traditional SRAM solutions. For chip designers, that’s an open invitation to rethink silicon real estate—more cores, more memory, smaller chips, or a leaner balance of all three.
It’s not just about saving space or energy; it’s about unlocking strategic performance advantages in AI-heavy environments where milliseconds—and millimeters—can translate into millions of dollars.
A Strategic Memory Stack: Beyond Just SRAM
This launch builds on Marvell’s existing custom memory technologies, including CXL-based integrations for adding terabytes of supplemental memory and compute to cloud servers, and custom HBM (High Bandwidth Memory) tech that boosts capacity by 33% without increasing chip footprint.
The new SRAM also boasts up to 3.75 GHz operating speed and leads the industry in bandwidth per square millimeter. The design, featuring an innovative data path and architecture, is tailor-made for the accelerated infrastructure of tomorrow.
Custom Chips for a Post-Moore’s Law World
The writing’s been on the silicon wall for years: Moore’s Law is slowing down. Shrinking transistors no longer guarantees cost-effective performance gains. In response, Marvell is leaning hard into customization, giving customers the tools to design chips that directly match their infrastructure needs.
This isn’t just a one-off product. The Marvell custom platform strategy blends system-level design with advanced manufacturing, offering tools like SerDes, die-to-die interconnects, silicon photonics, and PCIe Gen 7 compute fabrics to support holistic custom silicon development.
“Custom is the future of AI infrastructure,” says Will Chu, SVP of Custom Cloud Solutions at Marvell. “We’re seeing hyperscaler methods trickle down to more customers and more use cases.”
What the Experts Are Saying
Alan Weckel, co-founder of the 650 Group, highlights the central challenge Marvell is tackling: “Memory remains one of the biggest pain points in AI clusters and clouds. Marvell’s top-down approach—from chip to system—is compelling and illustrates the performance gains achievable through full-stack customization.”
Industry Context: Racing Ahead in AI Infrastructure
With this move, Marvell continues to position itself at the heart of the AI infrastructure arms race. Competitors like NVIDIA and AMD are similarly advancing custom silicon strategies—especially in memory and interconnect tech—but Marvell’s edge lies in its modular, collaborative platform approach, giving clients greater control over how to optimize for power, speed, or cost.
As AI workloads grow more specialized and infrastructure demands diversify, expect the custom silicon trend to expand beyond hyperscalers to enterprises, OEMs, and emerging players building for edge, 5G, and next-gen automotive.
Final Word
Marvell’s 2nm custom SRAM isn’t just about faster memory—it’s about a new chapter in chip design. One where performance isn’t squeezed from smaller transistors alone, but from smarter, customizable architectures that think beyond the core.
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