In the modern digital world, online shopping becomes essential in human lives. Online shopping stores like Amazon show up the "Frequently Bought Together" for their customers in their portal to increase sales. Discovering frequent patterns is a fundamental task in Data Mining that find the frequently bought items together. Many transactional data were collected every day, and finding frequent itemsets from the massive datasets using the classical algorithms requires more processing time and I/O cost. A GPU accelerated Novel algorithm for finding the frequent patterns using Vertical Data Format (GNVDF) has been introduced in this research article. It uses a novel pattern formation. In this, the candidate i-itemsets is divided into two buckets viz., Bucket-1 and Bucket-2. Bucket-1 contain all the possible items to form candidate-(i+1) itemsets. Bucket-2 has the items that cannot include in the candidate-(i+1) itemsets. It compactly employs a jagged array to minimize the memory requirement and remove common transactions among the frequent 1-itemsets. It also utilizes a vertical representation of data for efficiently extracting the frequent itemsets by scanning the database only once. Further, it is GPU-accelerated for speeding up the execution of the algorithm. The proposed algorithm was implemented with and without GPU usage and compared. The comparison result revealed that GNVDF with GPU acceleration is faster by 90 to 135 times than the method without GPU.