feat: TEE whitepaper with GPU/TPU analysis and deployment workflow#1
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feat: TEE whitepaper with GPU/TPU analysis and deployment workflow#1
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- Manual dispatch with cpu/gpu and plan/apply/destroy options - Validates terraform on push to main - Connects to VibeBrowser billing at api.vibebrowser.app - Verifies TEE attestation after deployment
…expand to 30 citations
dzianisv
changed the title
- Split deploy-tee.yml into deploy-tee-cpu.yml and deploy-tee-gpu.yml - CPU TEE: tee.vibebrowser.app (Intel TDX, ~$216/month) - GPU TEE: tee-gpu.vibebrowser.app (NVIDIA H100 CC, ~$6,300/month) - Add comprehensive open-weight models table (DeepSeek, Llama, Qwen, Mistral, Kimi K2, MiniMax, etc.) - GPU workflow includes model size selection (7B-70B) - Separate Cloudflare tunnel tokens per endpoint
- Add cpu-tee-amd-sev.tf: AMD SEV-SNP on DCasv5 (~$140/mo) - Add cpu-tee-intel-tdx.tf: Intel TDX on DCesv5 (~$216/mo) - Update whitepaper with platform comparison table - AMD SEV-SNP recommended for cost-sensitive deployments - Both platforms provide equivalent security guarantees
- Title: Privacy-Preserving LLM Inference with Hardware-Attested TEEs - Author: Dzianis Vashchuk only
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Summary
Comprehensive update to the TrustedGenAi whitepaper and infrastructure.
Changes
Whitepaper Updates
Infrastructure
Repository Cleanup
Overleaf
The whitepaper is synced to Overleaf and compiles to 11 pages: