Systems and methods for grouping embedding vectors in a storage device for improved read latency

A hardware storage device, comprising:

• a first location;
• and
• a second location,
• wherein, the hardware storage device is configured to:
• receive an inference query request comprising a first access index to locate a first embedding vector, the first embedding vector representing an object for processing by a machine-learning (ML) model;
• determine, from a first output of a classification model, to search the first location for the first embedding vector;
• search for the first embedding vector in the first location;
• receive a second access index to locate a second embedding vector;
• determine, from a second output of the classification model, to search the second location for the second embedding vector;
• and
• search for the second embedding vector in the second location.

A system, comprising:

• a processor;
• and
• a memory storing instructions, which, when executed by the processor, cause the processor to perform:
• receiving an inference query request comprising a first access index to locate a first embedding vector, the first embedding vector representing an object for processing by a machine-learning (ML) model;
• determining, from a first output of a classification model, a first location of the first embedding vector;
• searching for the first embedding vector in the first location;
• receiving a second access index to locate a second embedding vector;
• determining, from a second output of the classification model, a second location of the second embedding vector;
• and
• searching for the second embedding vector in the second location.