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Introduction: The Modern Imperative for Optimized AI Inference The rapid evolution of artificial intelligence has created a significant divide between the environments used for model training and those required for Read More …
The Paradigm Shift to the Edge The proliferation of connected devices and the exponential growth of data are fundamentally reshaping the architecture of artificial intelligence. The traditional, cloud-centric model, where Read More …
The Imperative for Model Compression on Consumer Hardware The field of artificial intelligence is currently defined by the remarkable and accelerating capabilities of Large Language Models (LLMs). These models, however, Read More …
Executive Summary Edge computing represents a foundational paradigm shift in digital infrastructure, moving computation and data storage from centralized cloud data centers to the logical extremes of a network, closer Read More …
The Federated Learning Paradigm and its Scaling Imperative 1.1. Introduction to the FL Principle: Moving Computation to the Data The traditional paradigm of machine learning has long been predicated on Read More …
Section 1: The Inference Hardware Landscape: An Executive Overview 1.1 The Core Challenge: Moving from Training to Production Inference The transition of a machine learning model from the research and Read More …
Executive Summary The proliferation of the Internet of Things (IoT) and the demand for real-time, privacy-preserving artificial intelligence (AI) have catalyzed a paradigm shift from cloud-centric computation to on-device AI, Read More …
Section 1: The Foundational Pillars of a New Technological Paradigm The confluence of fifth-generation wireless technology (5G), edge computing, and artificial intelligence (AI) represents a seminal shift in the digital Read More …
The Paradigm Shift to Compact AI: Defining the SLM Landscape From Brute Force to Finesse: The Evolution Beyond LLMs The trajectory of artificial intelligence over the past half-decade has been Read More …