NVIDIA reduces transformer-based compute challenges

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The continued growth of data processing and the emergence of computationally complex transformer models have created multiple challenges for organizations looking to maximize the value of their proprietary data. Organizations interviewed by Nucleus noted obstacles, including an 80 to 300 percent increase in annual cloud billing, extended latency limiting the effectiveness of real-time models, and exceptionally long training for novel transformer models. These challenges highlight the need for dedicated hardware supporting AI workloads and the cost of inefficiency. New hardware generations continue to emerge, and organizations must continuously reevaluate their infrastructure to optimize their current workloads.

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