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Google unveiled TurboQuant, a method that cuts the memory bottleneck slowing large AI models
Companies running large language models face a persistent bottleneck: the memory consumed by key-value caches during inference grows with every token generated, forcing operators to choose between ...
RDVQ is a VQ-based generative image compression framework for efficient and controllable ultra-low-bitrate image compression. Conventional VQ-VAE learns powerful discrete representations, but its ...
Abstract: A new data structure for efficient similarity search in very large datasets of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted ...
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