Categories: AI/ML Research

Effective KV Compression with TurboQuant

TurboQuant has recently been launched by Google as a novel algorithmic suite and library for applying advanced quantization and compression to large language models (LLMs) and vector search engines — an indispensable element of RAG systems.

Prompt Compression for LLM Generation Optimization and Cost Reduction

Large language models (LLMs) are mainly trained to generate text responses to user queries or prompts, with complex reasoning under the hood that not only involves language generation by predicting each next token in the output sequence, but also entails a deep understanding of the linguistic patterns surrounding the user…

December 2, 2025

In "AI/ML Research"

Here are 3 critical LLM compression strategies to supercharge AI performance

November 10, 2024

In "AI/ML News"

On Information Geometry and Iterative Optimization in Model Compression: Operator Factorization

The ever-increasing parameter counts of deep learning models necessitate effective compression techniques for deployment on resource-constrained devices. This paper explores the application of information geometry, the study of density-induced metrics on parameter spaces, to analyze existing methods within the space of model compression, primarily focusing on operator factorization. Adopting this…

July 22, 2025

In "FAANG"

AI Generated Robotic Content