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Tag: gpu

  • NVIDIA vs. Broadcom: Future Direction

    NVIDIA vs. Broadcom: Future Directions (April 2025) NVIDIA: Future Direction NVIDIA’s future strategy is deeply rooted in its leadership in accelerated computing, aiming to be the foundational platform for the AI era across diverse industries. Their vision extends beyond just selling chips to providing a comprehensive ecosystem of hardware and software. Continued Advancement in AI Read more

  • Top 20 Azure Cloud Interview Questions and Detailed Answers

    Top 20 Azure Cloud Interview Questions and Detailed Answers I. Core Azure Concepts & Services 1. Explain Microsoft Azure in your own words. What are its key benefits? Azure is Microsoft’s comprehensive set of cloud services that allows you to build, deploy, and manage applications and services through a global network of Microsoft-managed data centers. Read more

  • Developing Generative AI Applications with Microservices

    Microservices architecture, with its focus on building applications as a suite of small, independent services, offers a compelling approach to developing complex Generative AI applications. By breaking down the intricate workflows of GenAI into manageable components, microservices can enhance scalability, flexibility, and maintainability. 1. Why Microservices for Generative AI? 2. Potential Microservices for a Generative Read more

  • GPU vs. XPU vs. CPU: A Comparative Analysis

    In the world of computing, the terms CPU (Central Processing Unit) and GPU (Graphics Processing Unit) are commonly understood. However, the term XPU is emerging, representing a broader category of processing units. This analysis compares these three types of processors. 1. Central Processing Unit (CPU) The CPU is the brain of the computer, responsible for Read more

  • Comparative Analysis: Building Generative AI Applications in AWS, GCP, and Azure

    Generative AI is a rapidly advancing field, and the major cloud providers – Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure – are heavily investing in services and infrastructure to support its development and deployment. This analysis compares their key offerings for building generative AI applications. 1. Foundation Models and Model Hubs Read more

  • Comparative Analysis: Building AI Applications in AWS, GCP, and Azure

    Building Artificial Intelligence (AI) applications requires robust infrastructure, powerful compute resources, comprehensive toolkits, and scalable services. Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure are the leading cloud providers, each offering a rich set of AI and Machine Learning (ML) services. This analysis compares their key offerings and approaches for building AI Read more

  • Developing Aptitude and Skills for an AI-Focused Tech Career

    A career in Artificial Intelligence is dynamic and rewarding, but requires a specific blend of aptitude and learned skills. This guide outlines key areas to focus on to develop the necessary foundation for success in the AI-driven tech landscape. 1. Strengthen Your Foundational Aptitude While skills can be learned, certain inherent aptitudes can significantly accelerate Read more

  • Building a Product Manual Chatbot with Amazon OpenSearch and Open-Source LLMs

    This article guides you through building an intelligent chatbot that can answer questions based on your product manuals, leveraging the power of Amazon OpenSearch for semantic search and open-source Large Language Models (LLMs) for generating informative responses. This approach provides a cost-effective and customizable solution without relying on Amazon Bedrock. The Challenge: Navigating through lengthy Read more

  • RAG with locally running LLM

    Sample code to enable running the LLM locally. This will involve using a local LLM instead of OpenAI. Key Changes: To run this code with a local LLM: Important Considerations: Read more

  • Implementing RAG with vector database

    Explanation: Key Points: Remember to: Read more