Super Protocol

Forget central servers — Super SWARM by @Super_Protocol brings autonomous, verifiable compute to the next level 🧠 📽 Watch the demo: youtu.be/cazkNLGtE3c #ConfidentialComputing #TEE #Web3 #AI

"Confidential Computing is super important." – Jensen Huang, NVIDIA GTC 2026 At GTC 2026, Confidential Computing is placed right at the center of the NVIDIA AI Platform – between Blackwell and Rubin, as part of the foundation. To scale AI globally, you must protect everything – even from the infrastructure operator itself. That's the stack we've been building. Super Swarm: open-source by design, self-organizing CC clusters. NVIDIA provides the hardware. Super makes it deployable – any cloud, on-prem, hybrid, and even air-gapped environments. Verifiable by any party, at any time. 🎥 nvidia.com/gtc/keynote on CC (1:02:30) 🔗 superprotocol.com #GTC2026 #NVIDIA #ConfidentialComputing #TEE #AIInfrastructure #Blackwell #VeraRubin

Yesterday at OC3, the confidential computing ecosystem shared its insights. Our COO Yulia Gontar joined the Confidential Computing Consortium (CCC) to showcase the real-world impact of verifiable AI. We brought six projects that solve a universal structural problem: AI workloads require scalable high-performance compute but cannot afford to expose sensitive data or proprietary models to the provider, or any other participant. The Proof Grid (as presented at OC3): 🔹 Clinical AI: MedGemma-27B achieving a 9.4/10 doctor score inside a verifiably confidential environment. 🔹 Smart Hospital: Real-time EHR-to-Clinician AI on NVIDIA Blackwell (B200) via Nebius. 🔹 FDA Compliance: Cutting AI audit submissions from 4 weeks to 2 hours. 🔹 AdTech: Unlocking 319% growth on external training data for Mars & Realeyes. 🔹 Inter-Institutional AI: Centralized training on decentralized data (Brain Cancer ML in USA) – without exposing a single byte. 🔹 Self-Sovereign AI Cloud: Turning GPU fleets into verifiable environments across cloud and Hyperscalers, like Google – borderless. The Next Level: Super Swarm – the HTTPS layer for AI. Verifiable autonomous execution that no party can override. Your Choice of Infrastructure: Our protocol is designed for total flexibility without vendor lock-in. Whether you operate In the Cloud, On-Premise, or in a Hybrid environment, you can scale your AI whenever you need it. This also unlocks one more thing: the latest TEE-enabled hardware – like NVIDIA Blackwell – is available to you the moment you need it, with the exact same verifiable privacy guarantees across the board. And as you are waiting for the NVIDIA Vera Rubin launch – so are we! 60 seconds. Six proofs. Check below. PS: @ConfidentialC2 and and Rachel Wan, Outreach Vice Chair of CCC, thank you so much for making us part of your speech!

Sovereign cloud usually means one thing: data stays inside the jurisdiction. That's necessary. But it's not sufficient. Jurisdiction defines where data must stay. Compliance defines what the provider is allowed to do with it. But what if the provider simply cannot access it – technically, not just contractually? That's a different kind of sovereignty. Not a promise. An architectural guarantee. The demo shows how self-organizing confidential clusters work. The same approach applies if your infrastructure spans different types – on-prem or any cloud setup, single perimeter or distributed datacenters, locked to a specific jurisdiction if required. Including hybrid, when you need to scale out to public cloud with the same security guarantees. 👉 For the complete demo, visit: 🔗youtube.com/watch?v=jAH9-C…

The early web ran on HTTP. Data moved in plain text. Anyone controlling the infrastructure could read everything – passwords, transactions, records. HTTPS fixed that. Not by trusting the providers more. By making it impossible for them to read the traffic at all. Today, AI has the same problem. Your data, your model, your inference – processed on infrastructure you do not own, by providers you can only trust by contract. We are building the HTTPS layer for AI – based on Super Swarm. On March 12 the confidential computing ecosystem meets at the Open Confidential Computing Conference (OC3) 2026. As a @ConfidentialC2 member, we are bringing six projects to the conversation. 👉 superprotocol.com

Building proprietary AI is solved. Deploying it safely at scale? That too. For sensitive industries, the bottleneck is inference. The moment your model and user data must run on infrastructure you don't control, but still depend on for scale. That's the Inference Trust Gap. Until recently, deployment stagnated at the same structural point: to process complex workloads at scale, you need public cloud compute. But you cannot expose proprietary model weights or sensitive records to the infrastructure provider. That constraint no longer has to define the architecture. We ran a benchmark to validate this directly: MedGemma-27B (@GoogleDeepMind) on a single B200 GPU (hosted at @nebiusai) with Super Protocol enabling verifiable confidential execution. MedGemma-27B requires ~54GB VRAM for weights alone. On an H100 (80GB), that leaves minimal headroom for 128K-context workloads at production concurrency. The @nvidia B200 (192GB) changes the equation. • 64.2 tokens/sec – production throughput • 128K context window – approximately 300–400 pages of medical history per call • Input data remains inaccessible to the cloud provider throughout execution • Model weights, including proprietary fine-tuning, remain protected This is not just about speed. It is about architectural separation: the cloud provides compute. Execution governance is enforced independently, through hardware attestation – not policy or administrative trust. Performance, scale, and verifiable confidentiality. Without choosing between them. Check how the full stack works: @vllm_project, TEE-based hardware isolation, and Super Protocol's execution governance layer 👉 superprotocol.com/resources/infe…

Can you ensure that your LLM deployment is truly confidential? Large LLMs require significant GPU resources. GPU cloud providers make that compute accessible. But when proprietary model weights or third-party data are involved, deployment becomes more than just infrastructure. Confidentiality at runtime should not rely on trust in the operator, nor should it introduce operational complexity. Super Swarm builds on the core Super Protocol principles, with a redesigned confidential infrastructure layer ready for autonomous AI at scale. To demonstrate how this works in practice, we recorded a new Super Swarm walkthrough covering the full confidential LLM deployment flow – from cluster creation and LLM deployment to independent verification. Using an inference workload as the example, the walkthrough shows: - confidential cluster launch - LLM deployment on cloud GPUs - automatic generation of Deployment Evidence (cryptographic proof that the environment has not been altered) - secure model access via both API and application endpoints, with verification preserved in both cases In previous posts, we discussed the importance of decoupling execution control from infrastructure as the foundation of verifiable confidential AI. Now you can see it in action. 👉 For the complete demo, visit: 🔗 youtube.com/watch?v=GfVSwv… 👉👉 Bookmark the Super Swarm demo series to see additional use cases in action: 🔗🔗 youtube.com/playlist?list=…

Confidential fine-tuning on external data is not just about isolation. The real question is whether training runs under conditions no single participant can alter – and whether that can be independently verified. When external data is involved, hardware isolation alone is not enough. Data owners require enforceable guarantees that execution cannot be modified or overridden by any party – including the cloud provider. This is exactly where GPU clouds either become trusted compute platforms for sensitive AI – or remain generic capacity providers. TEE isolation protects data-in-use. But isolation alone does not enable collaboration across organizations. Fine-tuning on external data requires something fundamentally stronger: provable architectural sovereignty – where execution is governed by cryptographic rules rather than administrative control. Super Protocol adds a verifiable confidential execution layer on top of existing GPU cloud infrastructure. The cloud continues to provide GPU capacity and operate hardware. What changes is how execution is governed. Execution approval becomes architectural and cryptographic – not administrative. Compute supply and execution authority are structurally decoupled. Training proceeds only when predefined conditions are automatically validated through hardware attestation and workload verification. If they are not met, execution does not start. After completion, independent parties can verify that the training ran as intended – without requiring privileged access to the infrastructure. In this model, the GPU cloud supplies compute – but execution conditions cannot be altered by any single party, including the cloud provider or Super itself. That shift is what allows GPU clouds to host confidential fine-tuning across independent organizations – without requiring data transfer or centralized trust. This architecture enabled Realeyes to break the fine-tuning deadlock. They gained access to 319% more sensitive training data – resulting in measurable improvements in model quality and deeper insights for global ad optimization. 👉 Check case study: 🔗 superprotocol.com/case-studies/r…

Modern GPUs are becoming standard. What sets clouds apart now is how AI runs on them. Super Protocol turns #NVIDIA H100, H200, and Blackwell GPU fleets into verifiable, privacy-preserving AI clouds. It rolls out as a ready-to-run layer on top of existing cloud infrastructure, handling environment attestation, policy enforcement, and integrity checks end-to-end – without requiring providers to redesign their stack. For customers, it feels like a standard AI cloud with familiar tooling and workflows. The difference is architectural: workloads run in confidential mode and are automatically verifiable. Open-source by design, Super Protocol removes vendor lock-in and enables collaboration across clouds under the same provable privacy guarantees. For sensitive and regulated workloads, this is what makes cloud deployment possible. Without verifiable execution, sensitive AI remains limited to isolated pilots, on-prem infrastructure, or tightly controlled environments. With it, entire ecosystems can operate on shared GPU infrastructure. In one real-world healthcare project, this brought together: 🔹 a GPU cloud provider 🔹 a medical AI solutions provider 🔹 an EHR provider 🔹 and clinics running AI on live clinical data – All without exposing patient records, proprietary model logic, or relying on policy-based trust. Super Protocol acts as a neutral, verifiable execution layer across the stack, enabling each party to operate on shared GPU infrastructure while retaining control over its own data, models, and compliance boundaries. That is what makes GPU clouds ready for sensitive #AI workloads. 👉 Check case study: 🔗 superprotocol.com/case-studies/y… #ConfidentialComputing #AIInfrastructure #GPUCloud #TEE

AI is not a standard SaaS tool. With agentic systems, the security model breaks even faster. Traditional incidents assume clear ownership, clear boundaries, and clear responsibility. AI incidents don't. Who owns the data used during inference? Who controls the outputs? Who is accountable when models collaborate across teams or organizations? Confidentiality becomes the core challenge, and not performance. And governance becomes a new discipline entirely. Clients don't want promises. They want assurance that their data stays protected during execution. That's the difference between running AI, and running AI responsibly. Watch the full podcast. Link in the comments.

Explore the full case right now (superprotocol.com/case-studies/c…)

Model architecture is no longer the limiting factor in medical AI. Its real bottleneck today is access to real clinical data. To be clinically useful, models must learn from real clinical dialogues, yet those datasets are among the most sensitive and heavily regulated. Thanks to @super__protocol, Yma Health, @nvidia , @AMD and @GoogleResearch this trade-off was removed entirely. The outcome: a 9.4/10 recommendation score from practicing clinicians, strong clinical accuracy, and safer, more concise outputs compared to general-purpose LLMs. The MedGemma 27B model was fine-tuned on real clinical conversations inside a verifiable confidential execution environment based on H200 GPUs and AMD SEV-SNP. Data was decrypted only inside TEE, encryption keys never left the trusted boundary, and the execution environment was deleted after training. Clinicians evaluated the fine-tuned model in real clinical scenarios. During both training and inference, data and model access remained confined to the trusted execution environment. This case goes beyond healthcare. It demonstrates that: - privacy and scale are no longer mutually exclusive; - trust in AI can be cryptographically verifiable, not contractual; - sensitive-data training is possible without compromise. This case makes one thing clear: medical AI is moving from experimentation to production-grade infrastructure inside TEEs.

“If you are not in the LLM answer, you don’t exist for the user.” - Vlad Pivnev, CEO of ICODA That’s Vlad Pivnev, CEO of ICODA on how discovery is shifting from links to model answers, and why trust signals matter as much as rankings. Watch the full episode: youtu.be/3HW1I5558x4 @super__protocol #AI #LLM #AISearch

The New episode with Pavel Salas (CEO, SocialWisdom): “Orchestrating AI Agents for Trading & the Future of Web3.” We unpack why specialized agent stacks beat a single general model (signals → data validation → risk → execution), how this connects to smart contracts and DeFi, and where KYC/GDPR + privacy vs transparency become the real bottlenecks. 🎧 youtu.be/jhy_wqWMrFs