Run sensitive AI jobs on self-serve confidential GPU compute with Enclave

Enclave is a self-serve confidential GPU compute platform for teams that want to run sensitive AI or ML workloads without treating the infrastructure operator as fully trusted.

The practical hook is simple: you deploy a WebAssembly app from the browser, choose CPU and GPU shares, pay per second in ETH or USDC, then verify CPU and GPU attestation before sending data. The site exposes the important security details instead of hiding them behind a sales form: AMD SEV-SNP on the current fleet, NVIDIA GPU confidential-computing mode, TLS key binding inside the enclave, and a browser-verifiable attestation flow.

Use it when you need a short-lived endpoint for private inference experiments, sensitive data processing, customer demos, or proof-of-concept work where ordinary GPU rental feels too exposed. It is also useful as a research target for teams evaluating whether confidential GPU workloads fit their threat model.

What to check before using it:

• Verify attestation yourself before sending private data.
• Start with a small top-up because payments are final and unspent runtime is not withdrawable.
• Read the shared-GPU caveat: operator isolation is hardware-backed, but co-tenant isolation on shared cards depends on Wasm sandboxing, process boundaries, and the NVIDIA driver rather than per-tenant hardware partitioning.
• Use a full-GPU deployment when your threat model requires no co-tenants on the card.
• Treat the service as beta infrastructure until you have tested deployment, logs, costs, and failure behavior with non-critical workloads.

Visible pricing on the Enclave page lists GPU share at $6/hour for a full card plus CPU share at $3/hour for a full node, metered per second with whole-percent shares. The vendor gives example points such as 10% GPU plus 5% CPU at about $0.75/hour and 100% GPU plus 10% CPU at about $6.30/hour.

This is a TOOL candidate, not a blog story: the value is a concrete resource builders can try, evaluate, or bookmark when confidential AI compute matters.