OpenTMP LLM orchestrates multi‑party data and compute via MPC‑FL, enabling cross‑organization training and private inference — Private Efficient Governable collaborative AI.
CPU / GPU / NPU compatible · Edge inference & P2P training
On‑device training & encrypted inference.
Edge optimization, distillation & quantization.
MPC governance & shared ownership.
Break data silos and unlock collaboration without data exposure. Institutions co‑build shared model capabilities that meet compliance while optimizing performance and cost.
Risk/AML/credit scoring with data retained by each institution.
Robots learn locally and share experience securely.
User data remains private with authorized, auditable access.
Fair collaboration ensured by MPC/TSS.
A unified distributed LLM framework for edge training, private inference, and governable collaboration.
Multi‑node coordination; elastic orchestration.
Encrypted compute; zero‑trust collaboration.
Parameter ownership & authorization.
Risk, AML, and credit scoring with measurable contribution & governance.
Incremental learning locally; encrypted experience sharing across devices.
Private inference inside enterprises or devices with auditable access.
Co-building collaborative AI with robotics and edge partners, e.g., RobinX and TAI Phone (examples).
Note: Papers are representative references; implementation follows the engineering of OpenTMP LLM.
Bring collaborative intelligence to your chain, robotics, or agent network.
Prefer email for security-sensitive or partnership inquiries.