The objective of the proposed research is to address the inefficiency of conventional Von Neumann architectures in processing the ultra-large artificial intelligence (AI) models with memory-centric hardware accelerator. The exponential growth of data in genomics and AI has highlighted the limitations of conventional Von Neumann architectures, particularly their inefficiency in handling data-intensive applications. These systems suffer from high energy consumption and latency due to excessive data movement between memory and processing units. To address these challenges, this research proposes memory-centric hardware accelerator designs using 3D NAND Flash and 3D DRAM technologies. The goal is to develop novel architectures that leverage in-memory and near-memory computing to minimize data movement and improve energy efficiency and scalability. The proposed designs include a 3D NAND Flash-based hyperdimensional computing (HDC) engine for genome sequencing and mass spectrometry, and a 3D DRAM-centric accelerator for large language model (LLM) fine-tuning. By performing computations directly within or near memory arrays, these architectures aim to overcome the computational bottlenecks of traditional systems. Simulation results will benchmark the proposed designs against existing GPU-based accelerators, focusing on key metrics such as energy consumption, latency, memory density, and computational throughput. This research contributes to the development of efficient and scalable memory architectures for data-intensive applications in genomics and AI.