Scaling up Privacy-Preserving ML: A CKKS Implementation of Llama-2-7B

This project represents a significant technical milestone by scaling CKKS-based FHE to a 7-billion parameter model, which is historically computationally prohibitive. The '0 stars' vs '9 forks' suggests this is a fresh academic release (linked to arXiv:2601.18511v1) rather than a production tool. The moat is purely technical depth; implementing FHE for Transformers requires managing noise growth and the specific 'outlier' activation problem inherent in LLMs. However, defensibility is limited by the lack of a broader developer ecosystem or easy-to-use library wrapper. It competes with established FHE players like Zama (Concrete-ML) and Microsoft SEAL/OpenFHE. While frontier labs are interested in privacy, they are currently prioritizing TEEs (Trusted Execution Environments) over FHE due to the massive 100x-1000x latency penalty of FHE. The primary threat comes from specialized FHE acceleration startups and hardware-accelerated FHE implementations that could make this specific CKKS approach obsolete within 1-2 years.