Google's TurboQuant algorithm compresses LLM key-value caches to 3 bits with no accuracy loss. Memory stocks fell within ...

Make AI work smarter, not harder.

A small error-correction signal keeps compressed vectors accurate, enabling broader, more precise AI retrieval.

Google Research recently revealed TurboQuant, a compression algorithm that reduces the memory footprint of large language ...

With TurboQuant, Google promises 'massive compression for large language models.' ...

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

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

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 ...

Artificial intelligence model compression startup Refiant AI said today it has raised $5 million in seed funding from VoLo Earth Ventures to try to put an end to the “arms race” that has ignited a ...

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 ...