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SpliTEE: Improving LLM Inference on Trusted Hardware with Differentially Private GPU Outsourcing

arXiv · AI, language, vision and robotics · article · Sep 14, 2026 · UTC

User prompts provided to large language models (LLMs) may contain sensitive or private information that can be misused by remotely deployed models, such as through inadvertent memorization during retraining. One way to protect user prompts is to execute the LLM inside a trusted execution environment (TEE), with the guarantee that the service provider has no access to computations performed within or information exchanged with the TEE. However, current TEEs are primarily CPU-based and significantly slower than GPUs optimized for LLM inference. To circumvent this, Tramer and Boneh (2019) propose

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