XDA Developers on MSN

TurboQuant tackles the hidden memory problem that's been limiting your local LLMs

A paper from Google could make local LLMs even easier to run.

Google's TurboQuant compression tech cuts LLM memory use by 6x with no accuracy loss

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI ...

Google AI breakthrough shows why we don't need more data centers

That much was clear in 2025, when we first saw China's DeepSeek — a slimmer, lighter LLM that required way less data center ...
The Next Web

Google’s new compression algorithm cut memory stocks within hours of publication

Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...
IEEE

Lossless Compression and Data Transformation Techniques on QR Code Binary Bit Stream

Abstract: This paper presents a dataset-level evaluation of six lossless compression and data transformation techniques applied to visual-cryptographic (VC) shares derived from QR codes. We processed ...
IEEE

Identifying Important Data Transformations for Synthesizing Effective Lossless Compressors

Abstract: Efficient data compression is essential in high-performance computing, particularly when managing large-scale datasets. Lossless compression algorithms generally incorporate multiple stages, ...