Vector quantisation and its associated learning algorithms form an essential framework within modern machine learning, providing interpretable and computationally efficient methods for data ...

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

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

SAN FRANCISCO--(BUSINESS WIRE)--Elastic (NYSE: ESTC), the Search AI Company, announced new performance and cost-efficiency breakthroughs with two significant enhancements to its vector search. Users ...

Google researchers have published a new quantization technique called TurboQuant that compresses the key-value (KV) cache in large language models to 3.5 bits per channel, cutting memory consumption ...

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 ...