The Quantization Horizon: Navigating the Transition to INT4, FP4, and Sub-2-Bit Architectures in Large Language Models

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 …

Comprehensive Report on Quantization, Pruning, and Model Compression Techniques for Large Language Models (LLMs)

Executive Summary and Strategic Recommendations The deployment of state-of-the-art Large Language Models (LLMs) is fundamentally constrained by their extreme scale, resulting in prohibitive computational costs, vast memory footprints, and limited Read More …

A Comprehensive Analysis of Post-Training Quantization Strategies for Large Language Models: GPTQ, AWQ, and GGUF

Executive Summary The proliferation of Large Language Models (LLMs) has been constrained by their immense computational and memory requirements, making efficient inference a critical area of research and development. Post-Training Read More …