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GPU-less, Trust-less, Limit-less: Reimagining the Confidential AI Cloud - Mike Bursell
Takeaway
TEE-based confidential AI plus a decentralized marketplace (Super Protocol) enables training and inference on sensitive data without trusting any cloud provider.
Summary
- Trusted Execution Environments (Intel TDX, AMD SEV-SNP, Nvidia GPU TEEs) protect model and data in memory during processing with cryptographic attestation.
- Confidential AI unlocks use cases stuck on data access: medical model training, personal AI agents, regulated digital marketing, and model monetization without weight leakage.
- Super Protocol: TEE-agnostic, decentralized confidential AI cloud orchestrated by BNB-chain smart contracts; validated ARM v9 compatibility for edge.
- 'GPU-less' = vendor-independent GPU access via marketplace; 'limitless' = removes legal/jurisdictional blockers via provable guarantees.
- Attestation provides provenance — proves training ran inside verified hardware on declared datasets.
confidential-computingteesprivacy
Original description
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/