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

InformationalInfrastructureTechnology

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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Why RAG is Important vs Fine-Tuning and Training

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In the swiftly changing realm of artificial intelligence, companies are pursuing the most effective methods to optimise Large Language Models (LLMs) for their specific needs. Although conventional techniques like fine-tuning and comprehensive training are prevalent, Retrieval-Augmented Generation (RAG) is developing as a more efficient and pragmatic alternative. This essay will examine the significance of RAG, […]

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The Impact of Artificial Intelligence on Modern Data Centre Architecture

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The advent of Artificial Intelligence (AI) is causing a profound and fundamental shift in numerous industries around the globe, and the data centre business is a prominent illustration of this transformation. The computing requirements of AI workloads have caused a fundamental change in the design of data centres, requiring substantial adjustments in infrastructure, power needs, […]

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The Balance Between Training and Inferencing: Embracing a Hybrid GPU and CPU Approach

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InfrastructureTechnology

Understanding Training and Inferencing Training and inferencing are two central ideas in the field of machine learning and artificial intelligence, and they have different purposes and prerequisites. Models can be trained to improve their predictive and decision-making abilities by analysing data and adjusting their parameters in response to that analysis. To process large datasets and […]

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