Cascade: Token-Sharded Private LLM Inference

Cascade addresses the critical 'honest-but-curious' cloud provider problem by using MPC to ensure neither the user's prompt nor the model weights are exposed in plaintext. With 0 stars and 6 forks, this is currently an academic research artifact rather than a production-ready tool. Its defensibility is low because, while the math is complex, the implementation lacks the ecosystem or integration needed for a moat. It faces a massive headwind from hardware-based privacy (TEE/Confidential Computing) like NVIDIA's H100/Blackwell security features, which offer much lower latency overhead than software-based MPC. Specific competitors include projects like MPCFormer, BOLT, and Iron. Frontier labs are unlikely to adopt MPC for general use due to the 10x-100x latency penalty, but might keep it as a niche offering for extreme-privacy sectors (gov/defense). The 'token-sharding' aspect is a clever optimization but likely an incremental improvement over existing layer-wise MPC sharding.