What happens when private AI models or sensitive data need to run in the public cloud?

Can we still maintain control – without relying on blind trust? Can we eliminate that blind trust and make infrastructure verifiable by design?

In this talk, you’ll discover what a “GPU-less” future really means: not the absence of acceleration, but the freedom to collaborate and deploy private AII workloads in a confidential, self-sovereign AI cloud – with open, on-chain guarantees that centralized clouds simply can’t offer.

No GPU-provider lock-in. No black-box execution. Just algorithmic, sovereign infrastructure – where the confidential cloud is a protocol, not a service.

You’ll learn the foundations of Confidential AI and see real-world results powered by it. Then, through four demos on Super Protocol, you’ll learn how to:

1. AI Marketplace & Confidentiality Check – Deploy models in a few clicks and verify on-chain they’re running inside hardware-backed confidential environments.
2. n8n Healthcare AI Workflow – Build and run agentic automations for sensitive data – entirely within confidential environments.
3. Distributed vLLM Inference – Parallelize LLM inference across multiple GPU servers– with zero data exposure and no dependency on any single provider.
4. Provable Medical-Data Training & On-Chain Reporting – Train on multiple sensitive datasets inside confidential environments – no data or IP exposed to participants, infrastructure providers, or Super Protocol – and generate verifiable on-chain proofs of exactly what ran, where, and how.

Join us to discover how you can leverage Confidential AI today – and unlock new possibilities.

Extra resources: - NVIDIA on Super Protocol: https://developer.nvidia.com/blog/exploring-the-case-of-super-protocol-with-self-sovereign-ai-and-nvidia-confidential-computing - Website https://superprotocol.com/ - Super AI Marketplace: https://marketplace.superprotocol.com/ - Documentation: https://docs.superprotocol.com/