Tom's Hardware on MSN

Google's TurboQuant reduces AI LLM cache memory capacity requirements by at least six times

The algorithm achieves up to an eight-times performance boost over unquantized keys on Nvidia H100 GPUs.
Forbes

How Mixed-Precision Quantization Could Break AI’s Power Addiction

It turns out the rapid growth of AI has a massive downside: namely, spiraling power consumption, strained infrastructure and runaway environmental damage. It’s clear the status quo won’t cut it ...
Hackaday

Making The Smallest And Dumbest LLM With Extreme Quantization

The reason why large language models are called ‘large’ is not because of how smart they are, but as a factor of their sheer size in bytes. At billions of parameters at four bytes each, they pose a ...
WinBuzzer

Google’s TurboQuant Algorithm Slashes LLM Memory Use by 6x

Google has published TurboQuant, a KV cache compression algorithm that cuts LLM memory usage by 6x with zero accuracy loss, ...
Forbes

Quantum Acceleration For Frontier AI: Does It Cut The Cost Of Training?

Training frontier-scale transformers has become a significant source of financial exposure for enterprises. GPU shortages, power and cooling ceilings and rising cloud costs mean each serious ...
Hosted on MSN

Maximizing self-hosted LLM performance with limited VRAM

Large language models (LLMs) are increasingly everywhere. Copilot, ChatGPT, and others are now so ubiquitous that you almost can’t use a website without being exposed to some form of "artificial ...
Business Wire

Japan Tobacco and D-Wave Announce Quantum Proof-of-Concept Outperforms Classical Results for LLM Training in Drug Discovery

PALO ALTO, Calif.--(BUSINESS WIRE)--D-Wave Quantum Inc. (NYSE: QBTS) (“D-Wave” or the “Company”), a leader in quantum computing systems, software, and services, and the pharmaceutical division of ...