Glyph: Scaling Context Windows via Visual-Text Compression

Core Question This Article Answers: How can large language models (LLMs) process million-token contexts without prohibitive computational and memory costs? In the era of advanced AI, LLMs power everything from document analysis to multi-step reasoning. Yet, as contexts stretch to hundreds of thousands or millions of tokens, the quadratic complexity of attention mechanisms balloons resource demands, making real-world deployment impractical. Glyph offers a fresh solution: by rendering long texts into compact images and leveraging vision-language models (VLMs), it compresses inputs 3-4x while preserving accuracy. This approach not only extends effective context lengths but also accelerates training and inference. Drawing from …