Benchmarking AI Workloads on Intel Xeon with AMX: Best Practices and Performance Insights

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Introduction Artificial Intelligence (AI) workloads increasingly depend on robust computational resources, and Intel Xeon processors present an attractive solution for both training and inference. The introduction of Advanced Matrix Extensions (AMX) in Intel Xeon has significantly enhanced AI acceleration, especially for deep learning, natural language processing, and high-performance computing applications. Accurate benchmarking of these workloads […]

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The Decline of TensorFlow and the Rise of PyTorch in AI Workloads: A Documented Shift

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TensorFlow, formerly the preeminent deep learning framework created by Google, has been eclipsed in recent years by PyTorch, developed by Facebook AI Research. Previously the unequivocal choice for researchers and corporations, TensorFlow has experienced a slow decrease as PyTorch has emerged as the favoured framework for AI workloads. This transition is seen not only in […]

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The Shift from Proprietary NVIDIA AI Solutions to Open Source Frameworks: Why Enterprises are Making the Move

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In recent years, artificial intelligence (AI) has emerged as a fundamental element of innovation across various industries. Businesses, ranging from startups to multinational businesses, have been significantly investing in AI to enhance efficiencies, get insights, and develop new goods and services. Historically, numerous AI workloads have been driven by NVIDIA’s proprietary technologies, which have become […]

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