AI and machine learning

When it comes to AI workloads, memory plays a crucial role in determining the overall performance of the system. Two prominent types of memory that are often considered for AI workloads are high-bandwidth memory (HBM) and double data rate (DDR) memory, specifically DDR5. Which memory is right for an AI workload depends on various factors, including the specific requirements of the AI algorithms, the scale of data processing and the overall system configuration. Both HBM3E and DDR5 offer significant advantages, and their suitability depends on the specific use case, budget and available hardware options. Micron offers the latest generation of HBM3E and DDR5.

HBM3E memory is the highest end solution in terms of bandwidth, speed and energy efficiency¹ due to its advanced architecture and high-bandwidth capabilities. DDR5 memory modules are generally more mainstream and cost-effective at scale than HBM solutions.

For AI workloads, the ideal storage solution depends on several factors. Key considerations should include speed, performance, capacity, reliability, endurance and scalability. The best storage solution for AI workloads depends on the specific demands of your applications, your budget and your overall system configuration. Micron can offer the best-in-class NVMe SSDs for your specific needs. The Micron 9400 NVMe SSD sets a new performance benchmark for PCIe^® Gen4 storage. It packs in up to 30TB of capacity and is designed for critical workloads like AI training, high-frequency trading and database acceleration. The Micron 6500 ION NVMe SSD is the ideal high-capacity solution for networked data lakes.

Hybrid AI, sometimes referred to as distributed AI, is a blend of cloud-based and edge-based AI systems.  

Currently, most AI systems are based in data centers and accessible via cloud services, offering the most potent performance for applications like generative AI. 

On the other hand, edge-based AI, also known as on-device AI, refers to an AI system where data processing occurs directly on the device (such as smartphones, cars, personal computers and IoT devices). This eliminates the need for all data to be continuously sent to and from the cloud, improving privacy, bandwidth and latency constraints, and it can also work when there is no connection. 

The hybrid approach distributes and coordinates AI workloads among cloud and edge devices. Balancing cloud-based AI models with models that run directly on devices allows for real-time, autonomous decisions at the edge, complemented by long-term analysis and improvement in the cloud. 

In essence, hybrid AI aims to provide the best user experiences by taking advantage of the respective strengths of both cloud-based and edge-based AI.