Considering poor performance caused by vague definition of small files in Hadoop Distributed File System (HDFS), Cut-off Point via Grey Relational Analysis (Cut-GAR) was presented to find the cut-off point between small files and large files, the relationship among the consumed memory of NameNode (M), speeds of MB of Uploaded Files per Second (MUFS), speeds of MB of Accessed Files per Second (MAFS) and file size was analyzed, the proper file sizes according to the three factors, were set respectively as FM, FMUFS and FMAFS. And then, grey relational analysis was taken to weight impacts of the three factors on file size while file size was treated as evaluated object, and M, MUFS and MAFS were employed as evaluated indexes, therefore the weight of evaluated index and relational degree of index-object were obtained. The outcome that the sum of FM, FMUFS, and FMAFS multiplied by the corresponding index weight was regarded the approximate optimal value of cut-off point. As experiment results demonstrate, Cut-GAR achieves a balance among M, MUFS, and MAFS, which improves the performance of small file processing.