In the race to adopt enterprise AI, many companies are focused on building high-quality models. But models aren’t the only thing that matters. In fact, the quality of your models and the value you get from them are directly tied to the hardware you use to implement them. You need powerful compute hardware to train or fine-tune your models, but also hardware at the edge for low-latency inference workloads. Until recently, implementing distributed AI hardware was something most businesses didn’t need to worry about. Only the very largest organizations had the budgets to pursue advanced machine learning. Now that AI…

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