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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Unraveling Transformer Neural Networks: The Engines Powering Advanced AI

FutureInformationalTechnology

Transformers. A phrase typically associated with innovative language models like OpenAI’s GPT-series or Google’s BERT, which you may have heard of if you keep up with the latest developments in machine learning and artificial intelligence. But what exactly are Transformer models, and how did they come to play such a significant role in contemporary AI? […]

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