AuddiaInc_IR
$AUUD The AI infrastructure market is evolving.
While much of the initial buildout has been driven by the enormous centralized computing requirements of model training, the continued growth of AI inference is creating demand for compute that is increasingly distributed and closer to the point of use.
LT350 is being positioned around that shift, with a model designed to support localized AI workloads across applications including healthcare, robotics, autonomous systems and other latency-sensitive environments.
Below Jeff Thramann discuss why the transition from training to inference is an important part of LT350’s long-term opportunity.
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