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

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

This is really where TurboQuant's innovations lie. Google claims that it can achieve quality similar to BF16 using just 3.5 ...

What Google's TurboQuant can and can't do for AI's spiraling cost ...

TurboQuant vector quantization targets KV cache bloat, aiming to cut LLM memory use by 6x while preserving benchmark accuracy ...

Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

SAN FRANCISCO--(BUSINESS WIRE)--Elastic (NYSE: ESTC), the Search AI Company, announced Better Binary Quantization (BBQ) in Elasticsearch. BBQ is a new quantization approach developed from insights ...

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 ...

DeepSeek-R1, released by a Chinese AI company, has the same performance as OpenAI's inference model o1, but its model data is open source. Unsloth, an AI development team run by two brothers, Daniel ...