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1. Executive Summary The computational trajectory of Large Language Models (LLMs) has reached a critical inflection point in the 2024-2025 timeframe. For nearly a decade, the industry operated under a Read More …
1. Introduction: The Memory Wall and the IO-Aware Paradigm Shift The trajectory of modern artificial intelligence, particularly within the domain of Large Language Models (LLMs), has been defined by a Read More …
The Tyranny of Quadratic Complexity: Deconstructing the Transformer Inference Bottleneck The Transformer architecture has become the de facto standard for state-of-the-art models across numerous domains, from natural language processing to Read More …
The Imperative for Efficiency in Model Adaptation The advent of large language models (LLMs) represents a paradigm shift in artificial intelligence, with foundation models pre-trained on vast datasets demonstrating remarkable Read More …
The Foundation: The KV Cache as a Double-Edged Sword The advent of Large Language Models (LLMs) based on the Transformer architecture has catalyzed a paradigm shift in artificial intelligence. Central Read More …
The Quadratic Barrier: Fundamental Constraints in Transformer Scaling The transformative success of Large Language Models (LLMs) is built upon the Transformer architecture, a design that excels at capturing complex dependencies Read More …
Section 1: The Quadratic Wall – Deconstructing the Scaling Limits of Self-Attention The remarkable success of Transformer architectures across a spectrum of artificial intelligence domains is rooted in the self-attention Read More …
Executive Summary The widespread adoption of large language models (LLMs) has brought a critical challenge to the forefront of inference engineering: managing the Key-Value (KV) cache. While the KV cache Read More …