Marvell Technology has unveiled the industry’s first 2nm custom SRAM, setting a new benchmark in memory density and power efficiency for high-performance computing and AI infrastructure. The new offering, announced on June 17, underscores Marvell’s evolving position, not just as a design services provider but as a strategic IP powerhouse in the custom silicon ecosystem.
Built on cutting-edge 2nm process technology, the SRAM delivers up to 6 gigabits of high-speed memory and achieves industry-leading bandwidth per square millimeter. Marvell reports that the design can reclaim up to 15% of the total die area and reduce standby power consumption by as much as 66%, a critical improvement in the thermal and power-sensitive environments of cloud AI clusters.
This release is the latest in a string of memory innovations from Marvell, following its custom CXL implementations, custom HBM solutions, and multi-die packaging technologies. Together, they reflect a holistic strategy aimed at solving one of the thorniest challenges in AI compute infrastructure: moving data as fast as it’s being generated, without blowing past power and space constraints.
Not Just a Services Model: Deep IP Under the Hood
Marvell’s announcement is also a quiet rebuttal to the common misconception that firms like Marvell and Broadcom operate purely as semiconductor “services” companies. While their business models often revolve around bespoke chip development for hyperscalers and OEMs, their leverage lies in proprietary IP portfolios, spanning SerDes, die-to-die interconnects, memory macros, chiplet packaging, SoC fabrics, optical I/O, and signal integrity know-how.
To build a custom AI or HPC chip at sub-3nm nodes, it takes more than engineering headcount and access to PDKs or EDA tools. The real differentiator is internal IP: optimized logic blocks, interconnect architectures, and refined design methodologies honed over years of silicon production. Marvell, like Broadcom, owns and continuously evolves these foundational blocks, giving it an advantage in time-to-market, power/performance tradeoffs, and system-level co-design.
In this context, the 2nm SRAM isn’t just a memory block. It’s a building block that accelerates the entire custom XPU ecosystem, allowing customers to pack more compute, shrink thermal envelopes, and align chip designs more closely with their infrastructure-level performance and TCO goals.
The Economics of Custom in the AI Era
Will Chu, SVP of Marvell’s Custom Cloud Solutions, put it clearly: “Custom is the future of AI infrastructure.” His statement reflects a broader shift in the semiconductor industry, where Moore’s Law has slowed, and companies are now extracting performance through architectural innovation and vertical integration rather than transistor scaling alone.
Alan Weckel of the 650 Group noted that Marvell’s memory-centric design strategy is especially relevant for AI: “These systems need as much memory as they can get, as fast as they can.” With AI models scaling exponentially in parameter count and inference throughput, SRAM, HBM, and CXL-based memory pools are no longer just add-ons, they’re core to system performance.
And yet, even with technical superiority, this remains a cut-throat segment. OEMs and hyperscalers routinely play Marvell, Broadcom, and leading Taiwanese custom ASIC houses against one another to extract pricing leverage and roadmap commitments. In such a landscape, Marvell’s deep IP library, proven co-design frameworks, and control over packaging and interconnects offer resilience, but not immunity.
Why This Matters
Marvell’s 2nm custom SRAM is not a standalone announcement, it is a signal of where custom silicon is headed. As AI clusters demand tighter integration between logic, memory, and communication layers, vendors will need to offer complete, vertically optimized technology stacks. This includes not only physical IP but also AI-tuned compilers, system simulation frameworks, thermal-aware placement flows, and chiplet-based reuse strategies.
With this launch, Marvell joins Broadcom in proving that custom doesn’t mean commodity. It means control over every watt, every nanosecond of latency, every square millimeter of silicon real estate. And that control, in the age of trillion-parameter models and AI-driven workloads, is exactly what the largest cloud providers and AI system builders are buying.
Beyond technical achievement, Marvell’s 2nm SRAM highlights why its custom silicon model remains economically viable in an industry often skeptical of bespoke ASIC economics. Despite narrower gross margins, the custom business is designed for scalability built on reusable IP blocks, co-design workflows, and customer-funded engineering. This strategy not only keeps development costs off Marvell’s balance sheet, but also turns each socket win into a long-term, high-leverage engagement.
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