Changing AI math could reduce the hardware burden, researchers show

Sophisticated AI models tend to require a lot of memory and take up a lot of storage space. One of the ways to reduce that ...
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 ...
Hackaday

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 ...
GitHub

TurboQuant - Online Vector Quantization with Near-optimal Distortion Rate - Paper Notes.md

Notifications You must be signed in to change notification settings Fork 0 Star 0 Code Pull requests Projects Security Insights Code Issues Pull requests Actions Files ...
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 ...
Edutopia

Making Use of a Worked Example to Improve Learning

It’s a familiar moment in math class—students are asked to solve a problem, and some jump in confidently while others freeze, unsure where to begin. When students don’t yet have a clear mental model ...
IEEE

Vector Quantization-Based Clustered Federated Learning With Global Feature Anchors for Improved Representation and Generalization

Abstract: Clustered federated learning (CFL) addresses the challenge of data heterogeneity in federated learning (FL) by customizing models for different groups of clients. However, existing CFL ...