AI At An Inflection Point Drives Storage And Memory Demand
The NVIDIA GTC showed the company's support for AI modeling and inference infrastructure as well as new software and service offerings. This infrastructure requires significant storage and memory to train and run these models. DDN showed their latest storage appliance for the DGX H100 systems.
What is driving the demand for storage and memory in AI?
The demand for storage and memory in AI is primarily driven by the need to train and run complex AI models. As AI applications expand across various industries, such as medical science and automotive design, they require substantial digital storage for modeling data and significant memory to process this data efficiently.
How does NVIDIA support AI infrastructure?
NVIDIA has introduced several products and services aimed at supporting AI infrastructure, including the GRACE CPU with 1TB of memory and the GRACE Hopper CPU Superchip designed for high-performance computing. These innovations are intended to provide significant performance improvements for applications that handle large datasets.
What role does DDN play in AI storage solutions?
DDN is addressing the storage needs of AI training models by offering their A3I storage appliances, which are compatible with NVIDIA's DGX systems. These appliances are designed to support high-speed throughput and large data models, enabling customers to scale their workloads effectively while utilizing advanced networking technologies.
AI At An Inflection Point Drives Storage And Memory Demand
published by Infusai
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