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    GPUs are designed to accelerate machine learning computations while simultaneously reducing latency and costs for training models and running inferences for production ML. While they are optimized to quickly process large workloads, unless they are managed efficiently, they can quickly drive up your consumption costs. This tech talk explores how you can efficiently use GPU resources for production inferences. We walk through some of the common approaches and potential pitfalls with using GPUs, and help you identify the most efficient and cost effective method to meet your team’s needs and resources.