Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to caching key-value vectors for fixed prefixes according to a caching policy. A system can comprise a memory that can store computer-executable components. The system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise an accessing component that can access a request comprising a fixed prefix and variable data. The system can further comprise a storage component that can selectively store in a cache, one or more key-value vectors generated via processing of the fixed prefix by a large language model (LLM), based on a temporal moving average of the fixed prefix being greater than a first defined threshold, according to a smart encoding policy.

Abstract: Embodiments of the invention provide a computer-implemented method that includes executing, using a generative language model, generative language model operations operable to generate an output sequence responsive to an original input sequence. The generative language model operations include token pruning operations that include performing a base set of token pruning operations on intermediate versions of the original input sequence; and performing token pruning (TP) constraint evaluations. The base set of token pruning operations identify pruning candidate tokens in the intermediate versions of the original input sequence. The TP constraint evaluations determine that at least one of the pruning candidate tokens will be pruned from an associated intermediate version of the original input sequence.

Abstract: One or more systems, devices, computer program products and/or computer-implemented methods of use provided herein relate to caching key-value vectors for fixed prefixes according to a caching policy. A system can comprise a memory that can store computer-executable components. The system can further comprise a processor that can execute the computer-executable components stored in the memory, wherein the computer-executable components can comprise an accessing component that can access a request comprising a fixed prefix and variable data. The system can further comprise a storage component that can selectively store in a cache, one or more key-value vectors generated via processing of the fixed prefix by a large language model (LLM), based on a temporal moving average of the fixed prefix being greater than a first defined threshold, according to a smart encoding policy.