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Performance of Confidential Computing GPUs
Antonio Martínez Ibarra, Julian James Stephen, Aurora González Vidal, K. R. Jayaram, Antonio Fernando Skarmeta Gómez
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This work examines latency, throughput, and other metrics when performing inference on confidential GPUs. We explore different traffic patterns and scheduling strategies using a single Virtual Machine with one NVIDIA H100 GPU, to perform relaxed batch inferences on multiple Large Language Models (LLMs), operating under the constraint of swapping models in and out of memory, which necessitates efficient control. The experiments simulate diverse real-world scenarios by varying parameters such as traffic load, traffic distribution patterns, scheduling strategies, and Service Level Agreement (SLA) requirements. The findings provide insights into the differences between confidential and non-confidential settings when performing inference in scenarios requiring active model swapping. Results indicate that in No-CC mode, relaxed batch inference with model swapping latency is 20-30% lower than in confidential mode. Additionally, SLA attainment is 15-20% higher in No-CC settings. Throughput in No-CC scenarios surpasses that of confidential mode by 45-70%, and GPU utilization is approximately 50% higher in No-CC environments. Overall, performance in the confidential setting is inferior to that in the No-CC scenario, primarily due to the additional encryption and decryption overhead required for loading models onto the GPU in confidential environments.
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