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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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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DeepSeek and the AI Hardware Market: A Disruptor or Just Another Competitor?

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The AI hardware market is rapidly evolving, driven by the increasing complexity of AI workloads. DeepSeek, a new large-scale AI model from China, has entered the scene, but its impact on the broader AI landscape remains an open question. Is it simply a competitor to OpenAI’s ChatGPT, or does it have wider implications for inferencing, […]

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Rising Electricity Costs: The Silent Barrier to AI Growth and Leadership

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The rapid advancement of artificial intelligence (AI) is reshaping industries and economies worldwide. However, the substantial electricity consumption required to develop and deploy AI technologies presents significant challenges, particularly in regions with high energy costs. This article explores how electricity expenses may impede AI growth and leadership globally, with a focus on the United Kingdom. […]

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