SMART ZDIMMs for AI and ML

2024-11-08
●Artificial Intelligence (AI) and Machine Learning (ML) computing applications ingest large amounts of data. Inferencing servers accept input data and execute AI and ML models developed in training servers. Training servers use advanced software to develop models for functions such as object and facial recognition, genomic and medical research. The output models from training servers are returned to the inferencing servers where they are put to work. Inferencing servers require ~20% more DRAM than standard servers whereas training servers require ~2.5 times more DRAM than standard servers.
●Using highly reliable DRAM memory for AI and ML applications is extremely critical. If there are too many memory ECC errors or other catastrophic DRAM bit failure issues this will cause a system shutdown and the entire training job needs to be restarted. This problem can be extremely costly and take significant time to recover. One solution used widely today to mitigate against memory failures is having redundant computing systems that mirror the AI and ML functions. As one can imagine that this is extremely costly.
●A new cost-optimized and highly effective solution is to use SMART’s high reliability ZDIMMs. ZDIMMs (Zefr Memory Modules) are rigorously tested to eliminate over 90% of memory reliability failures, ensuring maximum application uptime and optimizing memory subsystem reliability.

SMART

SRZAG8RD5846-SBSRZHG8RD5648-SPSRZ8G8RD5448-SPSRZ4G8RD5448-SPSRZ4G8RD5288-SPSRZ8197RD440425-SCSRZ4097RD440425-SCSRZ4097RD420825-SCSRZ2047RD410825-SE

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Part#

ZDIMMsDDR5 Registered ZDIMMDDR4 Registered ZDIMM

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AI ]ML ]Natural language processing ]LLMs ]Image recognition ]3D rendering ]security camera data processing ]DNA Sequencing ]high resolution medical image processing ]Algorithmic trading ]real time high-speed analytics ]

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Supplier and Product Introduction

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10.06.24

Rev.6

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