![]() ![]() ![]() These will not only demand discrete GPU systems, but even larger and more advanced GPU clusters for enhanced processing. This increase in demand will touch enviable horizons with 35% CAGR during said period!įuture AI/ DL projects will be more extensive in terms of scale and use cases. The semiconductor chip market (which includes GPUs, besides CPUs, ASICs, etc.) for exclusively running Deep Learning applications was pegged at USD 4.5 billion in 2020 and is expected to balloon to USD 81 billion by 2030. Thus, the dependence on Graphics Processing Units (GPUs) for accelerating AI/ DL workloads. Afterall, leveraging terabytes of data for model development and training is exceedingly resource-intensive. Industries that fail to adapt to technological upheavals like the proliferation of ML/ DL risk falling behind the competition.Įnterprises today comprehend that AI/ DL applications demand heavy computation capabilities. ![]() However, the constant evolution of Deep Learning applications pre-supposes powerful processing units. Forward-looking enterprises are solving a lot of tumultuous problems by harnessing AI/ ML in an appropriate way. Artificial Intelligence (AI) and Machine Learning (ML) are poised to spark innovation and upend existing research methodologies. ![]()
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