Organizations increasingly face the challenge of protecting sensitive data while collaborating, particularly when the data is in use and vulnerable to cyberattacks. Fully Homomorphic Encryption (FHE) emerges as a promising solution by allowing computations on encrypted data without decryption, thus preserving privacy, especially in machine learning applications. However, implementing FHE for machine learning is computationally intensive and requires substantial resources, particularly due to the need for bootstrapping. Hardware acceleration, using platforms like FPGA, GPU, and ASIC, is seen as a viable solution to improve efficiency and make FHE practical. The OpenFHE Library supports this by providing a community-driven, open-source cryptographic software framework, which includes a unique Hardware Abstraction Layer (HAL) to integrate different hardware acceleration technologies. This development opens opportunities for hardware providers to advance FHE applications, contributing to the growing trend of privacy-preserving machine learning.