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.

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are effectively massive vector spaces in ...

At 100 billion lookups/year, a server tied to Elasticache would spend more than 390 days of time in wasted cache time.

Even if you don’t know much about the inner workings of generative AI models, you probably know they need a lot of memory. Hence, it is currently almost impossible to buy a measly stick of RAM without ...

Google's new TurboQuant algorithm drastically cuts AI model memory needs, impacting memory chip stocks like SK Hynix and Kioxia. This innovation targets the AI's 'memory' cache, compressing it ...

Google thinks it's found the answer, and it doesn't require more or better hardware. Originally detailed in an April 2025 paper, TurboQuant is an advanced compression algorithm that’s going viral over ...

Seagate Technology Holdings plc is downgraded to hold due to near-term risks from energy prices & potential AI CapEx ...

The biggest memory burden for LLMs is the key-value cache, which stores conversational context as users interact with AI chatbots. The cache grows as conversations lengthen, ...

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

Alphabet is leading the way in driving down AI costs.

Any software that claims to be independent from hardware is inefficient, bloated software. The time for such software development is over.