Confidential LLM Inference in Production: Hardware TEEs, GPU Enclaves, Attestation, and Serving Performance Trade-Offs
Confidential LLM Inference in Production: Hardware TEEs, GPU Enclaves, Attestation, and Serving Performance Trade-Offs Deploying large language models in multi-tenant cloud environments introduces a fundamental security boundary problem. Standard transport encryption (TLS) secures prompts in transit, and encryption-at-rest protects checkpoints on disk, but model weights, prompt tokens, and key-value (KV) caches exist in plaintext within system memory during active inference. For organizations p
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