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 ...

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

Within 24 hours of the release, community members began porting the algorithm to popular local AI libraries like MLX for Apple Silicon and llama.cpp.
Nanowerk

Researchers create an algorithm that maximizes IoT sensor inference accuracy using edge computing

(Nanowerk News) We are in a fascinating era where even low-resource devices, such as Internet of Things (IoT) sensors, can use deep learning algorithms to tackle complex problems such as image ...

Industrial AI's Real Bottleneck Isn't the Algorithm

Walk through enough industrial AI deployments and a pattern becomes uncomfortable to ignore. The pilot works. The model ...

IndexCache, a new sparse attention optimizer, delivers 1.82x faster inference on long-context AI models

Researchers at Tsinghua University and Z.ai built IndexCache to eliminate redundant computation in sparse attention models ...

Google unveils TurboQuant, a new AI memory compression algorithm — and yes, the internet is calling it ‘Pied Piper’

Google’s TurboQuant has the internet joking about Pied Piper from HBO's "Silicon Valley." The compression algorithm promises ...

Why Google’s TurboQuant Algorithm is Disrupting the AI Memory Chip Market

Google's TurboQuant combines PolarQuant with Quantized Johnson-Lindenstrauss correction to shrink memory use, raising ...
The Verge

Meta’s new AI chips run faster than before

The next-generation MTIA chip could be expanded to train generative AI models. The next-generation MTIA chip could be expanded to train generative AI models. Meta promises the next generation of its ...