Multi-data center training is becoming essential for AI factories as pretraining scaling fuels the creation of even larger models, leading the demand for computing performance to outpace the capabilities of a single facility. By distributing workloads across multiple data centers, organizations can overcome limitations in power, cooling, and space, enabling the training of even larger…
]]>Embeddings play a key role in deep learning recommender models. They are used to map encoded categorical inputs in data to numerical values that can be processed by the math layers or multilayer perceptrons (MLPs). Embeddings often constitute most of the parameters in deep learning recommender models and can be quite large, even reaching into the terabyte scale. It can be difficult to fit…
]]>Join the NVIDIA Triton and NVIDIA TensorRT community to stay current on the latest product updates, bug fixes, content, best practices, and more. Deep learning is revolutionizing the way that industries are delivering products and services. These services include object detection, classification, and segmentation for computer vision, and text extraction, classification…
]]>If there’s one constant in AI and deep learning, it’s never-ending optimization to wring every possible bit of performance out of a given platform. Many inference applications benefit from reduced precision, whether it’s mixed precision for recurrent neural networks (RNNs) or INT8 for convolutional neural networks (CNNs), where applications can get 3x+ speedups. NVIDIA’s Turing architecture…
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