This article provides a playbook for scaling the Triton Inference Server across multiple GPUs within a Kubernetes environment. It addresses the challenges of running multiple production models on a single GPU under heavy traffic. The guide details strategies such as instance groups, dynamic batching, and other techniques to optimize performance and efficiency. AI
IMPACT Provides technical guidance for optimizing AI model serving infrastructure, potentially improving inference performance and cost-efficiency.
RANK_REASON The article is a technical guide or playbook for using and scaling an existing tool (Triton Inference Server) within a specific infrastructure (Kubernetes).
- Docker
- graphics processing unit
- Kubernetes
- MLOps
- NVIDIA
- ONNX Runtime
- PyTorch
- Tensorflow
- Triton Inference Server
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