Graphics processing unit (gpu) optimization using dynamic programming for generative artificial intelligence (ai) and large language models (llm)

A method comprising:

• at a computing platform comprising at least one processor, a communication interface, and memory:
• receiving matrix multiplication information indicating a plurality of matrix dimension sets for matrix multiplication;
• for each matrix dimension set:
• identifying one or more multiplication variations, indicating different possible orders of operation for executing the corresponding multiplication,
• performing memoization to identify, for each order of operation, a corresponding number of operations to complete the corresponding multiplication,
• identifying, based on the numbers of operations, a most efficient order of operation, and
• storing, in a lookup table, a relationship between the given matrix dimension set and the most efficient order of operation;
• receiving a request to configure a generative artificial intelligence (AI) model;
• identifying first matrix dimensions corresponding to the request;
• identifying, using the lookup table, a first order of operations corresponding to the first matrix dimensions;
• iteratively training the generative AI model based on the first order of operations;
• and
• deploying the generative AI model.

One or more non-transitory computer-readable media storing instructions that, when executed by a computing platform comprising at least one processor, a communication interface, and memory, cause the computing platform to:

• receive matrix multiplication information indicating a plurality of matrix dimension sets for matrix multiplication;
• for each matrix dimension set:
• identify one or more multiplication variations, indicating different possible orders of operation for executing the corresponding multiplication,
• perform memoization to identify, for each order of operation, a corresponding number of operations to complete the corresponding multiplication,
• identify, based on the numbers of operations, a most efficient order of operation, and
• store, in a lookup table, a relationship between the given matrix dimension set and the most efficient order of operation;
• receive a request to configure a generative artificial intelligence (AI) model;
• identify first matrix dimensions corresponding to the request;
• identify, using the lookup table, a first order of operations corresponding to the first matrix dimensions;
• iteratively train the generative AI model based on the first order of operations;
• and
• deploy the generative AI model.