What action is likely to resolve inconsistent inference latency observed on an NVIDIA T4 GPU?

What action is likely to resolve inconsistent inference latency observed on an NVIDIA T4 GPU?

• A. Deploy the model on a CPU
• B. Implement GPU isolation for inference process
• C. Increase the number of inference threads
• D. Upgrade the GPU driver

Answer: (B)

Implementing GPU isolation for the inference process is a well-founded approach to resolving inconsistent inference latency on an NVIDIA T4 GPU. This strategy involves allocating dedicated resources of the GPU specifically for inference tasks, which prevents interference from other processes that might be utilizing the GPU simultaneously. When multiple applications or processes compete for GPU resources, it can lead to fluctuation in performance and, consequently, inconsistent inference latency. By isolating the inference workload from other tasks, the inference predictions can be made more reliably and with consistent speed, as there is reduced contention for the GPU’s computational resources. This action is particularly effective in environments where workloads are dynamically changing or when multiple models are being served, ensuring that your inference tasks have a stable performance profile.

Implementing GPU isolation for the inference process is a well-founded approach to resolving inconsistent inference latency on an NVIDIA T4 GPU. This strategy involves allocating dedicated resources of the GPU specifically for inference tasks, which prevents interference from other processes that might be utilizing the GPU simultaneously. When multiple applications or processes compete for GPU resources, it can lead to fluctuation in performance and, consequently, inconsistent inference latency. By isolating the inference workload from other tasks, the inference predictions can be made more reliably and with consistent speed, as there is reduced contention for the GPU’s computational resources.

This action is particularly effective in environments where workloads are dynamically changing or when multiple models are being served, ensuring that your inference tasks have a stable performance profile.