Google has introduced TurboQuant, a compression algorithm that reduces large language model (LLM) memory usage by at least 6x while boosting performance, targeting one of AI's most persistent ...
The big picture: Google has developed three AI compression algorithms – TurboQuant, PolarQuant, and Quantized Johnson-Lindenstrauss – designed to significantly reduce the memory footprint of large ...
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 ...
The compression algorithm works by shrinking the data stored by large language models, with Google’s research finding that it can reduce memory usage by at least six times “with zero accuracy loss.” ...
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As foreigners post heavy net selling on the main board, the KOSPI is plunging more than 3%. Individuals and the national pension funds are net buying but failing to defend the index. In particular, ...
On March 27, the US International Trade Commission (ITC) launched an investigation into memory chip imports by SK Hynix Inc. and KIOXIA Holdings Corporation following a patent complaint... Samsung ...
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Google's TurboQuant reduces AI LLM cache memory capacity requirements by at least six times
Google Research published TurboQuant on Tuesday, a training-free compression algorithm that quantizes LLM KV caches down to 3 bits without any loss in model accuracy. In benchmarks on Nvidia H100 GPUs ...
RAM prices are enough to make you choke on your toast, so Google Research has turned up with TurboQuant to cram LLMs into less memory. TurboQuant is pitched as a compression trick for the key-value ...
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