Because the computational demands of model execution vary significantly, training and inference hardware architectures possess different sweet spots regarding both price and performance. Consequently, it is crucial to select hardware that matches the specific workload, balancing the high cost of training-optimized accelerators against the more affordable, streamlined hardware suitable for inference.

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Hardware accelerators optimized for deep learning inference are designed specifically to compute the forward propagation of a neural network. Because no intermediate data needs to be stored for backpropagation, these devices require significantly less memory capacity. Furthermore, inference tasks can typically tolerate lower numerical precision without heavily impacting predictions, allowing these accelerators to efficiently utilize formats like FP16 or INT8. For example, NVIDIA's Turing T4 GPUs are specifically tailored for these streamlined inference workloads.

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