Towards Encrypted Large Language Models with FHE

The article introduces a framework for implementing encrypted large language models (LLMs) using Fully Homomorphic Encryption (FHE). It details the architecture modifications necessary to enable efficient inference on encrypted data, achieving a speedup of 3.5x compared to previous methods while maintaining model accuracy. This advancement is significant for practitioners as it allows for secure processing of sensitive data without compromising the performance of LLMs, thereby enhancing privacy in AI applications.