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Sumeet Gagneja is senior vice president and chief financial officer at Rambus. He is responsible for the global finance organization, including financial strategy, capital allocation, accounting, treasury, tax, reporting and investor engagement, playing a key role in supporting the company’s long‑term profitable growth and driving stockholder value.
Achieved strong Q1 results, delivering quarterly product revenue of $88.0 million, up 15% year over year Generated strong quarterly cash from operations of $83.2 million Expanded product and IP offerings for next-generation AI platforms, including the LPDDR5X SOCAMM2 server module chipset, the industry’s fastest HBM4E memory controller IP SAN JOSE, Calif. – April 27, 2026 […]
As AI workloads continue to diversify, the systems that support them are evolving just as quickly. AI is no longer confined to the hyperscale data center. It is moving to the factory floor, into vehicles, and increasingly to the edge, where power, cost, and form factor constraints can matter just as much as raw performance.
Learn how SOCAMM2 brings LPDDR5X power efficiency and high bandwidth to scalable, serviceable AI server memory in this Ask the Experts video.
Download this product brief to learn more about the Rambus 3 Amp Voltage Regulator for Server SOCAMM2.
Download this product brief to learn more about the Rambus 12 Amp Voltage Regulator for Server SOCAMM2.
Rambus Voltage Regulators for LPDDR5X-based SOCAMM2 enable a broad range of performance and capacity configurations for AI servers.
SAN JOSE, Calif. — Apr. 22, 2026 — Rambus Inc. (NASDAQ: RMBS), a premier chip and silicon IP provider making data faster and safer, today announced a SOCAMM2 (Small Outline Compression Attached Memory Module) chipset designed to enable low-power, high-performance LPDDR5X-based memory modules for AI server platforms.
Supporting low-power, high-performance memory modules for AI, the Rambus SOCAMM2 Server Chipset enables the operation of LPDDR5X-based SOCAMM2 modules.
At Rambus, we spend a lot of time solving hard problems at the intersection of memory and compute. Bandwidth limits. Power integrity. Signal integrity. Thermal constraints. These challenges become even more complex as AI training, inference, and high-performance computing push memory subsystems to their limits. That is why industry recognition matters less as a trophy […]
