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Section 1: The Paradigm Shift: From Monolithic Models to Dynamic, Heterogeneous LLM Ecosystems 1.1 Deconstructing the Monolithic Model Fallacy: Cost, Latency, and Performance Bottlenecks The rapid proliferation and adoption of Read More …
Section 1: The Data Bottleneck as an Economic Liability The modern artificial intelligence (AI) economy is built on a single, critical commodity: data. High-quality, representative data is the foundational pillar Read More …
The Data-Gated Lifecycle: Why 90% of AI Prototypes Fail The contemporary boom in Artificial Intelligence (AI) is predicated on the dual pillars of algorithmic innovation and data availability. Yet, while Read More …
Executive Summary This report provides a comprehensive architectural analysis of two leading frameworks in the artificial intelligence (AI) ecosystem: Ray and Hugging Face Text Generation Inference (TGI). The central inquiry Read More …
Part I: The Paradox of Long Contexts: Expanding Windows, Diminishing Returns The field of Large Language Models (LLMs) is in the midst of a profound architectural transformation, characterized by a Read More …
Part I: The Foundational Challenges of LLM Inference The rapid ascent of Large Language Models (LLMs) from research curiosities to production-critical services has precipitated an equally rapid and necessary evolution Read More …
Executive Summary Model serving represents the critical final mile in the machine learning lifecycle, transforming a trained, static model into a dynamic, value-generating asset accessible to real-world applications. This process, Read More …