FHECore: Rethinking GPU Microarchitecture for Fully Homomorphic Encryption

FHECore represents a high-depth academic research project aiming to bridge the gap between slow software-based FHE on GPUs and rigid, long-lead-time ASICs. With 13 forks and 0 stars, it exhibits the typical footprint of a research repo used by collaborators or peer reviewers rather than a broad developer community. The defensibility is rooted in the extreme technical complexity of FHE math (NTT, modular reduction) and GPU microarchitecture, but it lacks a commercial moat. The primary threat is NVIDIA; if NVIDIA decides to add specific FHE-friendly instructions or functional units to future Tensor Cores, this research becomes a validation for their roadmap rather than a competing product. While frontier labs (OpenAI/Anthropic) are currently focused on LLM performance, FHE is a 'niche' privacy technology they are unlikely to build, keeping frontier risk low. However, the hardware market is extremely consolidated, and the displacement horizon is long due to the 3-5 year cycles of silicon fabrication and architectural shifts.