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.

Running a large language model is expensive, and a surprising amount of that cost comes down to memory, not computation.

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

AI has a growing memory problem. 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 ...

TurboQuant breakthrough: Google's TurboQuant compresses LLM KV-cache up to 6x without quality loss, freeing GPU memory and boosting inference speed. Hybrid attention savings: DeltaNet-style ...

Google's TurboQuant can dramatically reduce AI memory usage. TurboQuant is a response to the spiraling cost of AI. A positive outcome is making AI more accessible by lowering inference costs. With the ...

Google's TurboQuant reduces the KV cache of large language models to 3 bits. Accuracy is said to remain, speed to multiply. Google Research has published new technical details about its compression ...

Google LLC has unveiled a technology called TurboQuant that can speed up artificial intelligence models and lower their memory requirements. Amir Zandieh and Vahab Mirrokni, two of the researchers who ...

AI just found a way to use less memory. That does not mean memory will get cheaper. Google’s new technique, TurboQuant, is generating buzz for dramatically reducing how much memory AI models need ...