Konstantina155/InferONNX

InferONNX addresses a legitimate technical hurdle in confidential computing: the limited Enclave Page Cache (EPC) size in older Intel SGX hardware, which often cannot fit modern deep learning models. By automating model partitioning, it allows segments of a model to be swapped or executed in sequence within the secure enclave. However, with only 4 stars and no forks after a year, the project lacks any market traction. From a competitive standpoint, it is severely outclassed by more mature frameworks like Apache Teaclave (MesaTEE), Gramine, and specifically BlindAI (Mithril Security), which provides a production-grade secure ONNX server. Furthermore, the move toward Intel TDX (Trust Domain Extensions) and NVIDIA's Confidential Computing on H100 GPUs renders the specific 'SGX partitioning' approach increasingly obsolete, as newer hardware provides much larger protected memory regions. Platform risk is high because cloud providers (Azure, AWS) are integrating these capabilities directly into their confidential computing offerings (e.g., Azure Confidential Computing with ONNX support).