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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. 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 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 …
Introduction: The Opaque Mind of the Machine: From Black Boxes to Mechanistic Understanding The advent of large language models (LLMs) built upon the transformer architecture represents a watershed moment in 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 …