Samsung creates energy-efficient server memory for AI data centers
By Alimat Aliyeva
Samsung Electronics Co. announced on Thursday that it has developed low-power server memory solutions designed to support continuous artificial intelligence (AI) workloads at large-scale data centers, as the company aims to solidify its position in the rapidly growing AI infrastructure market, Azernews reports, citing Yonhap agency.
The company recently showcased samples of its new Small Outline Compression Attached Memory Module 2 (SOCAMM2)—a low-power double data rate (LPDDR)-based server memory module specifically engineered for AI data centers—to key customers, including Nvidia Corp., according to a post on Samsung Electronics’ newsroom.
Samsung highlighted that SOCAMM2 delivers higher bandwidth, improved power efficiency, and flexible system integration, enabling AI servers to operate with greater efficiency and scalability. The company emphasized that its advanced LPDDR technology, combined with close collaboration with Nvidia, positions it at the forefront of a market expected to become a critical AI memory segment alongside high bandwidth memory (HBM).
Through ongoing technical cooperation, Samsung is working with Nvidia to optimize SOCAMM2 for Nvidia’s accelerated computing infrastructure, ensuring the memory module meets the stringent responsiveness and efficiency requirements of next-generation AI inference platforms.
“Samsung has been contributing alongside key partners, helping to establish consistent design guidelines and enable smoother integration across future AI platforms,” the company said.
Industry experts note that memory modules like SOCAMM2 could play a pivotal role in powering the next wave of AI applications, from large language models to real-time data processing in cloud and edge computing environments. By offering high-performance memory with lower power consumption, Samsung is addressing one of the key bottlenecks in AI infrastructure: balancing performance, energy efficiency, and scalability for increasingly complex workloads.
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