XDA Developers on MSN

Google Opal solves the most serious problem non-coders face, and not even Claude or local ...

The only no-code automation platform deserves more attention than it gets ...
Tech Times

AI Model Compression for $1,000: Ora Computing Uses Quantum Physics to Beat Hardware Lock-In

Vienna startup Ora Computing raised €3.5M and proved a 70-billion-parameter large language model can be compressed for under ...
IEEE

Quantization-Error-Robust Deep Neural Network for Embedded Accelerators

Abstract: Quantization with low precision has become an essential technique for adopting deep neural networks in energy- and memory-constrained devices. However ...
Hackaday

TurboQuant: Reducing LLM Memory Usage With Vector Quantization

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...
VentureBeat

Google's new TurboQuant algorithm speeds up AI memory 8x, cutting costs by 50% or more

As Large Language Models (LLMs) expand their context windows to process massive documents and intricate conversations, they encounter a brutal hardware reality known as the "Key-Value (KV) cache ...
GitHub

[ICCV 2025] D³QE: Learning Discrete Distribution Discrepancy-aware Quantization Error for ...

Integrates dynamic codebook frequency statistics into a transformer attention module. Fuses semantic image features with latent representations of quantization ...
GitHub

LQER: Low-Rank Quantization Error Reconstruction for LLMs

LQER runs a high-rank low-precision GEMM and a group of low-rank high-precision GEMMs in parallel to push the limitation of lossless LLM PTQ. The DeepWok Lab, is an ML research group led by Dr. Aaron ...