Micron Technology announced it is shipping customer samples of its 192GB SOCAMM2 memory module. The new product, developed for AI data centers, uses LPDDR5X technology to increase capacity and performance while reducing power consumption.
The module, a Small Outline Compression Attached Memory Module (SOCAMM2), provides 192 gigabytes of capacity, the highest available for this form factor in data centers. This is a 50 percent capacity increase over the prior generation within an identical physical footprint. The high-density design is critical for space-constrained AI servers, enabling more memory per system to support large AI models. By concentrating capacity, the module directly addresses the escalating memory requirements of modern artificial intelligence workloads, which rely on vast datasets and extensive parameter counts to function effectively.
At its core, the SOCAMM2 uses LPDDR5X DRAM, a technology originally from the mobile sector now adapted for enterprise use. The memory is produced with Micron’s 1-gamma DRAM process, its most advanced manufacturing node. This process yields a power efficiency improvement of over 20 percent compared to previous generations. The combination of LPDDR5X’s low-power architecture with the advanced fabrication process creates a memory solution specifically engineered to reduce the significant energy demands of AI computation, transforming low-power DRAM into a data center-class component with enhanced robustness and scalability.
Performance metrics include data transfer speeds reaching up to 9.6 gigabits per second (Gbps), providing the throughput needed to prevent data bottlenecks in AI systems. A primary feature is its energy savings, with the module reducing power consumption by more than two-thirds compared to equivalent RDIMM (Registered Dual In-line Memory Module) deployments. As RDIMMs are the server standard, this reduction offers substantial operational savings. Lower power draw decreases electricity costs and lessens the strain on data center cooling systems, a major factor in total cost of ownership and infrastructure sustainability.
The increased memory capacity directly improves AI application performance, especially for real-time inference tasks. The availability of 192GB on one module can reduce the “time to first token” (TTFT) by more than 80 percent. TTFT is a key latency metric in generative AI, measuring the delay before a model begins generating a response. For interactive services such as AI assistants, this shortened delay is vital. The significant reduction in initial latency allows AI models to deliver output much faster, which enhances the responsiveness and user experience of these latency-sensitive applications.
The SOCAMM2 standard is inherently modular, a design that offers practical advantages for managing large computing environments. This modularity enhances server serviceability, allowing for easier and faster replacement or upgrading of individual memory modules with minimal system downtime. In large data center clusters, such streamlined maintenance is essential for maintaining high availability. The design also creates a clear path for future capacity expansion, enabling operators to scale their memory resources in alignment with the growing demands of next-generation AI models, thereby protecting hardware investments over time.
Development of the low-power server memory was a joint effort with Nvidia, conducted over a five-year period. This strategic partnership positions the SOCAMM2 as a key solution for next-generation AI platforms, and the collaboration suggests a design optimized for integration within the Nvidia ecosystem. The product is targeted specifically at the AI data center market, where memory demands are surging due to the rise of generative AI and massive-context models. These advanced AI systems require vast, fast, and highly efficient memory to operate effectively, a need the module is engineered to meet.
Micron has started customer sampling of the 192GB module, allowing partners to test and validate the technology in their own systems. High-volume production is scheduled to align with customer launch timelines to ensure market availability for new server deployments. The module’s considerable energy efficiency supports the broader data center industry’s shift toward more sustainable, power-optimized infrastructure. This focus helps operators manage both the financial and environmental costs associated with the rapid global expansion of artificial intelligence workloads and their associated hardware footprints.
