Which approach would be most suitable to deploy and manage a deep learning inference workload in a Kubernetes-managed cluster with NVIDIA A100 GPUs?

Deploying and managing a deep learning inference workload in a Kubernetes-managed cluster with NVIDIA A100 GPUs is best approached using NVIDIA Triton Inference Server with Kubernetes. Triton is specifically designed to optimize and simplify the deployment of machine learning models in production environments. It supports various frameworks, including TensorFlow, PyTorch, and ONNX, allowing for flexibility in model types.

By leveraging Kubernetes, Triton can scale automatically based on the workload demands, make use of the underlying GPU resources efficiently, and support multi-model serving seamlessly. This integration offers features like dynamic model management, efficient resource allocation, and advanced inferencing capabilities, ensuring high availability and performance for inference tasks.

In contrast, the other options do not provide the same level of integration and management for inference workloads. While the CUDA toolkit with Docker facilitates GPU programming and containerization, it lacks the specific capabilities for model inference management that Triton offers. Standalone TensorRT might provide high-performance inference but does not address orchestration and scalability within a Kubernetes environment. Lastly, Apache Kafka is more suitable for message brokering and stream processing, rather than directly managing the inference workload or utilizing the GPU resources efficiently.