---
title: "Confidential Computing"
description: "3 posts about Confidential Computing, summarised, each linking to the original."
---

# Confidential Computing
> 3 posts about Confidential Computing, summarised, each linking to the original.

## Articles

### [Self-encrypting VM images for confidential environments hands on](https://yomu.fyi/post/self-encrypting-vm-images-for-confidential-environments-hands-on.md)
- Company: [Red Hat](https://yomu.fyi/company/red-hat.md)
- Author: Vitaly Kuznetsov
- Published: Sep 24, 2026
- Type: Tutorial

Confidential VM technologies such as AMD SEV-SNP and Intel TDX protect runtime data but leave persistent storage protection to the guest OS. This article demonstrates a method using Fedora Rawhide that combines dm-verity integrity for the immutable base image with a dm-crypt encrypted overlay for mutable parts. The base /usr partition remains verity-protected, while on first boot systemd-repart creates an encrypted root partition with a key sealed to the virtual TPM, ensuring each VM gets a unique key. An overlay filesystem mounts the immutable base under the writable overlay, avoiding a full copy of the base image and reducing boot time significantly in cloud environments. The article also outlines current limitations, including the lack of proof of origin for the overlay partition and the absence of integrity or replay attack protection.


### [Bringing Private Processing to Meta AI Glasses](https://yomu.fyi/post/bringing-private-processing-to-meta-ai-glasses.md)
- Company: [Meta](https://yomu.fyi/company/meta.md)
- Author: By Pritam Shah, Oskar Linde
- Published: Sep 24, 2026
- Type: Announcement

Meta AI glasses require cloud processing for advanced AI features like streaming transcription and long-term recall, but traditional cloud architectures expose data during use. Private Processing uses confidential virtual machines with Trusted Execution Environments (TEEs) on CPUs and GPUs to keep data encrypted even while being computed. Before sending context, glasses perform remote attestation, verifying the server’s TEE certificate against a public transparency ledger that records all deployed binary images. Non-targetable routing via blind-signed tokens and third-party OHTTP relays prevents tying requests to specific users, while encrypted storage ensures data is only accessible with user-provided keys. This infrastructure, already proven for WhatsApp and Meta AI, extends to glasses, with security audits by firms like NCC Group and a bug bounty program for independent research.


### [Enabling Private High-Performance Production AI Inference with NVIDIA Confidential Computing](https://yomu.fyi/post/enabling-private-high-performance-production-ai-inference-with-nvidia.md)
- Company: [NVIDIA](https://yomu.fyi/company/nvidia-developer-blog.md)
- Author: Tanya Lenz
- Published: Sep 22, 2026
- Type: Explainer

NVIDIA Confidential Computing (CC) enables secure AI inference by using memory-encrypted confidential VMs, confidential GPUs, and encrypted NVLink. Running inference frameworks like TensorRT LLM in a CC environment introduces performance overhead because secure execution changes assumptions about memory movement, timing, and multi-GPU communication. To quantify this overhead, the post presents a controlled methodology comparing CC-on and CC-off performance on a workload with long input context, extended output generation, and low concurrency. With CC-aware adaptations—including pageable memory selection, asynchronous readback, GPU %globaltimer for autotuning, and CC-aware multi-GPU communication—TensorRT LLM retained 96.1–98.2% of baseline throughput on eight NVIDIA B200 GPUs while keeping per-token latency overhead below 5%. The post concludes that security configuration and inference optimization should be treated as a single deployment problem for organizations moving private inference into production.