Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study

• URL: http://arxiv.org/abs/2409.03992v4
• Date: Tue, 5 Nov 2024 16:57:26 GMT
• Title: Confidential Computing on NVIDIA Hopper GPUs: A Performance Benchmark Study
• Authors: Jianwei Zhu, Hang Yin, Peng Deng, Aline Almeida, Shunfan Zhou,
• Abstract summary: We benchmark the overhead introduced by TEE mode across various large language model (LLM) inference tasks. Our results indicate that while there is minimal computational overhead within the GPU, the overall performance penalty is primarily attributable to data transfer.
• Score: 11.306063471976369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This report evaluates the performance impact of enabling Trusted Execution Environments (TEE) on NVIDIA Hopper GPUs for large language model (LLM) inference tasks. We benchmark the overhead introduced by TEE mode across various LLMs and token lengths, with a particular focus on the bottleneck caused by CPU-GPU data transfers via PCIe. Our results indicate that while there is minimal computational overhead within the GPU, the overall performance penalty is primarily attributable to data transfer. For the majority of typical LLM queries, the overhead remains below 7%, with larger models and longer sequences experiencing nearly zero overhead.

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