HomeElectronics NewsMemory Chipset Upgrade Targets AI Servers

Memory Chipset Upgrade Targets AI Servers

A server memory chipset helps computers run artificial intelligence and cloud applications with higher memory speed and lower power use.

Renesas Gen 3 MRDIMM Chipset Solutions Advance DDR5 Memory Performance to 16,000 MT/s for Next-Gen AI and HPC Applications
Renesas Gen 3 MRDIMM Chipset Solutions Advance DDR5 Memory Performance to 16,000 MT/s for Next-Gen AI and HPC Applications

Renesas Electronics has introduced its third-generation (Gen 3) DDR5 Multiplexed Rank Dual In-Line Memory Module (MRDIMM) chipset, supporting server memory speeds of up to 16,000 MT/s. The new chipset is designed to increase memory bandwidth for artificial intelligence (AI), cloud computing, and other data-intensive server workloads.

The Gen 3 MRDIMM chipset includes the third-generation Multiplexed Registering Clock Driver (MRCD, RRG5013) and Multiplexed Data Buffer (MDB, RRG5103). Renesas will demonstrate these memory interface components at the Future of Memory and Storage (FMS) 2026 conference in Santa Clara, California, from August 4 to 6.

According to the company, the new chipset delivers 25% higher memory bandwidth than its previous-generation MRDIMM solution while continuing to work with existing DDR5 server infrastructure. This allows server manufacturers to improve system performance without changing the overall memory architecture.

The company also offers a complete MRDIMM platform that includes the MRCD, MDB, power management ICs (PMICs), Serial Presence Detect (SPD) hubs, and temperature sensors. This enables customers to reuse existing designs while upgrading to higher-performance memory modules.

The Gen 3 solution retains the standard DDR5 DIMM form factor and system compatibility introduced with the previous generation. It also adds Device Equalization Self-Train Mode (DESTM) Quality Indication Status, a feature that helps optimize memory timing and receiver equalization to improve signal quality and system reliability.

Renesas said the new MRDIMM chipset is also designed to improve system-level power efficiency, helping data center operators manage higher memory bandwidth without significantly increasing power consumption or thermal load. Detailed power specifications will be available in the product documentation.

 “AI training and inferencing workloads are fundamentally reshaping system memory requirements for data center infrastructure,” said Sameer Kuppahalli, Vice President and General Manager, Memory Interface Division at Renesas. “To meet the insatiable appetite of these workloads for memory capacity and throughput, Renesas continues to lead the industry by delivering our 3rd generation of chipset components for MRDIMM. Our customers employ Renesas’ complete chipset components in order to further push the frontiers of memory throughput and capacity.”

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Nidhi Agarwal
Nidhi Agarwal
Nidhi Agarwal is a Senior Technology Journalist at Electronics For You, specialising in embedded systems, development boards, and IoT cloud solutions. With a Master’s degree in Signal Processing, she combines strong technical knowledge with hands-on industry experience to deliver clear, insightful, and application-focused content. Nidhi began her career in engineering roles, working as a Product Engineer at Makerdemy, where she gained practical exposure to IoT systems, development platforms, and real-world implementation challenges. She has also worked as an IoT intern and robotics developer, building a solid foundation in hardware-software integration and emerging technologies. Before transitioning fully into technology journalism, she spent several years in academia as an Assistant Professor and Lecturer, teaching electronics and related subjects. This background reflects in her writing, which is structured, easy to understand, and highly educational for both students and professionals. At Electronics For You, Nidhi covers a wide range of topics including embedded development, cloud-connected devices, and next-generation electronics platforms. Her work focuses on simplifying complex technologies while maintaining technical accuracy, helping engineers, developers, and learners stay updated in a rapidly evolving ecosystem.

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