What would be the most effective strategy to mitigate latency spikes during real-time inference on a Kubernetes cluster with NVIDIA GPUs?

Using NVIDIA Triton Inference Server with Dynamic Batching is the most effective strategy to mitigate latency spikes during real-time inference on a Kubernetes cluster with NVIDIA GPUs due to its ability to optimize the utilization of GPU resources.

Dynamic batching allows the inference server to group multiple incoming requests into a single batch when hardware resources are available. This reduces the overhead of processing individual requests and improves throughput, effectively smoothing out latency spikes during times of high demand. The server intelligently manages the batching window and the requests, allowing for efficient resource use, which can help in scenarios where incoming requests are variable and unpredictable in nature.

This strategy leverages Triton’s advantage of being designed specifically for deploying AI models at scale, along with its capability to handle various model formats, which contributes to reducing latency. It ensures that the GPUs are not idly waiting on requests but are utilized efficiently, thereby decreasing the average response time observed by clients.

In contrast, deploying the model on Multi-Instance GPU (MIG) Architecture focuses more on resource isolation and maximizing multi-tenant GPU utilization rather than directly addressing latency. Increasing the number of replicas in the Kubernetes cluster can help handle more concurrent requests but may not effectively mitigate latency spikes for each individual transaction, especially if the underlying bottleneck is the