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Executive Summary The rapid evolution of Transformer-based Large Language Models (LLMs) has fundamentally altered the landscape of artificial intelligence, transitioning from simple pattern matching to complex reasoning, code generation, and 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 …
1. Executive Synthesis: The Interplay of Memory, Mechanism, and Measurement The contemporary field of generative artificial intelligence is defined by a fundamental conflict. On one side, market and enterprise demands 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 Anatomy of LLM Inference and Its Intrinsic Bottlenecks The deployment of Large Language Models (LLM) in production environments has shifted the focus of the machine learning community from training-centric 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 …