This research evaluates how effectively large language models can operate in secure, isolated computing environments that protect both model IP and user data.

• DeepSeek-7B model successfully deployed in AMD SEV-SNP secure environment with minimal performance impact
• Demonstrated practical feasibility of running complex LLMs within protected hardware enclaves
• Identified key performance optimizations that maintain security while reducing latency
• Established a benchmark methodology for evaluating AI models in confidential computing contexts

This work addresses critical security concerns for enterprise AI deployment by showing how companies can leverage trusted execution environments to provide strong guarantees for model confidentiality and data privacy—essential for regulated industries and sensitive applications.

Evaluating the Performance of the DeepSeek Model in Confidential Computing Environment