A computer-implemented method of optimizing or improving parameters for grouping differently sized object input/outputs, IOs, for key-value solid state drives, the method performed by a system having a target device , a training circuit and an inferencing circuit , the method comprising: receiving, by the target device , differently sized object IOs for the target device ; grouping, by the target device , the IOs using a first plurality of input parameters; associating, by the target device , a tracking parameter with the first plurality of input parameters and with a performance parameter corresponding to the first plurality of input parameters; storing, by the target device , a first data entry comprising the tracking parameter , the first plurality of input parameters, and the performance parameter in a database ; extracting, by the training circuit , a plurality of data entries which include the first data entry from the database ; training , by the training circuit , a training model using one or more of the plurality of data entries; cross-validating , by the training circuit , the training model to determine error reduction of the training model; performing , by the training circuit , a model check to compare the error reduction of the training model to error reduction of an inferencing model; if the error reduction of the training model is greater than the error reduction of the inferencing model, then updating , by the training circuit , the inferencing model based on the training model; using , by the inferencing circuit , the updated inferencing model to infer suitable input parameters as a response to the performance parameter being outside of an expected range ; performing , by the inferencing circuit , a parameter check by comparing the inferred input parameters to the input parameters of the target device ; and if the inferred input parameters are different from the input parameters of the target device , then updating, by the inferencing circuit , the input parameters of the target device . receiving, by the target device , differently sized object IOs for the target device ; grouping, by the target device , the IOs using a first plurality of input parameters; associating, by the target device , a tracking parameter with the first plurality of input parameters and with a performance parameter corresponding to the first plurality of input parameters; storing, by the target device , a first data entry comprising the tracking parameter , the first plurality of input parameters, and the performance parameter in a database ; extracting, by the training circuit , a plurality of data entries which include the first data entry from the database ; training , by the training circuit , a training model using one or more of the plurality of data entries; cross-validating , by the training circuit , the training model to determine error reduction of the training model; performing , by the training circuit , a model check to compare the error reduction of the training model to error reduction of an inferencing model; if the error reduction of the training model is greater than the error reduction of the inferencing model, then updating , by the training circuit , the inferencing model based on the training model; using , by the inferencing circuit , the updated inferencing model to infer suitable input parameters as a response to the performance parameter being outside of an expected range ; performing , by the inferencing circuit , a parameter check by comparing the inferred input parameters to the input parameters of the target device ; and if the inferred input parameters are different from the input parameters of the target device , then updating, by the inferencing circuit , the input parameters of the target device . <EOS>