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1.0 The Paradigm Shift: From Recurrence to Parallel Self-Attention Prior to 2017, the field of sequence modeling and transduction was dominated by complex recurrent neural networks (RNNs), specifically Long Short-Term Read More …
Executive Summary The capacity of Large Language Models (LLMs) to process and reason over extensive sequences of information—a capability defined by their “context window”—has become a pivotal frontier in artificial Read More …
The Tyranny of Quadratic Complexity: Deconstructing the Transformer Attention Bottleneck The Transformer architecture, a cornerstone of modern artificial intelligence, is powered by the self-attention mechanism. While remarkably effective, this mechanism Read More …
Introduction: The Shifting Landscape of Sequence Modeling The field of sequence modeling was fundamentally reshaped in 2017 with the introduction of the Transformer architecture. Its core innovation, the self-attention mechanism, 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 Artificial Intelligence (AI) accelerator market in 2025 is defined by a strategic divergence between the industry’s two principal architects. Nvidia’s Blackwell architecture extends its market dominance through Read More …
Section 1: The Principle of Conditional Computation and the Genesis of Mixture of Depths The development of the Mixture of Depths (MoD) architecture represents a significant milestone in the ongoing Read More …