Samsung Electronics’ SOCAMM2 / Captured from Samsung Electronics official web site
Samsung Electronics is in search of to regain its management within the reminiscence market with its newest low-power server reminiscence module, the small define compression hooked up reminiscence module 2 (SOCAMM2), by supplying samples to a serious buyer as a part of the ultimate stage earlier than mass manufacturing.
Samsung Electronics just lately revealed that it started supplying its SOCAMM2 samples to a serious shopper firm. The corporate didn’t specify the shopper or particulars of the validation progress, however trade officers assume the samples are buyer samples for Nvidia’s next-generation Vera Rubin synthetic intelligence (AI) platforms.
Chip samples are sometimes categorized into three levels: working die, engineering pattern and buyer pattern. A working die refers back to the earliest useful stage of a chip. An engineering pattern is an early preproduction model despatched to clients to confirm compatibility with their methods and merchandise. After reflecting buyer suggestions, a near-final model often called a buyer pattern is delivered for qualification. As soon as the qualification course of is accomplished, the chip goes into mass manufacturing.
SOCAMM2 is a module of low-power double information fee (LPDDR) reminiscence chips, geared toward chopping energy consumption to roughly one-third of standard DDR-based modules. In keeping with a Shinhan Securities report, making use of the most recent LPDDR5X to servers can scale back energy consumption by about 77 % in contrast with DDR5, whereas rising bandwidth by 35 %.
Whereas high-bandwidth reminiscence (HBM) chips are often mounted on the graphics processing unit (GPU) for sooner information processing, SOCAMM2 is often mounted subsequent to the CPU and focuses on dealing with massive information volumes with better energy effectivity. On this setup, HBM helps computing acceleration, whereas SOCAMM2 is accountable for bettering general system-level energy effectivity.
The semiconductor trade is now working to arrange a normal for SOCAMM2, and an rising variety of chip builders are being attentive to the module’s energy effectivity as AI service corporations scramble to cut back the large quantity of electrical energy consumed by information facilities.
In keeping with Samsung, its 192-gigabyte SOCAMM2 is 57 % smaller than standard DDR-based server modules and delivers greater than twice the bandwidth whereas consuming no less than 55 % much less energy. The corporate famous that this allows steady efficiency even below intense AI workloads and provides excessive design flexibility, permitting it to be extensively deployed throughout a spread of AI methods.
In contrast to conventional soldered LPDDR options, SOCAMM2 permits straightforward reminiscence upgrades or replacements with none mainboard modification, serving to system directors decrease downtime and dramatically scale back the whole price of possession.
To satisfy rising market demand for low-power reminiscence, Samsung Electronics stated it’s in search of to increase the ecosystem for LPDDR-based server reminiscence, significantly by collaboration with Nvidia to optimize SOCAMM2 for Nvidia’s acceleration infrastructure.
Together with Samsung, reminiscence giants SK hynix and Micron are additionally scrambling to produce SOCAMM2 for Nvidia.
SK hynix is reportedly present process pattern validation for its 192-gigabyte SOCAMM2 product at Nvidia, whereas Micron has already delivered its 192-gigabyte SOCAMM2 to the market. Nevertheless, trade officers stated Samsung has secured the most important share within the Nvidia-bound provide thus far, as a result of firm’s technical collaboration with Nvidia and its superior provide capability in contrast with its rivals.
“Because the period of AI inference accelerates, system efficiency is prone to be decided not by the GPU’s uncooked computing energy however by how reminiscence bottlenecks are addressed,” an trade official stated. “As main cloud service suppliers at the moment are prioritizing energy effectivity for CPUs and reminiscences, SOCAMM2 could be seen as the place to begin of a brand new AI reminiscence cycle.”
