Model Serving Infrastructure
High-performance inference serving systems for production ML deployment.
Triton Inference Server
NVIDIA Triton implements a high-performance inference serving system with multi-framework support and dynamic batching. It provides sophisticated model management with versioning and concurrent model execution. The system includes advanced features like adaptive batching with request timeout handling and custom backend support. Features include automated model optimization through TensorRT integration and quantization. Implements efficient sequence batching for stateful models and ensemble model pipelines with cross-framework execution.
TorchServe
TorchServe implements PyTorch model serving with sophisticated model management and multi-model serving capabilities. It provides dynamic batching with configurable batch size and latency constraints. The system includes advanced features like model versioning, A/B testing, and custom handlers for pre/post processing. Features include automatic model optimization through TorchScript and quantization. Implements efficient GPU utilization with multi-GPU support and automatic model placement.
KServe
KServe implements cloud-native model serving on Kubernetes with sophisticated traffic management and autoscaling. It provides canary deployments and automated rollbacks based on metrics. The system includes advanced features like request routing, traffic splitting, and model explainability integration. Features include automatic model conversion through ONNX and serverless deployment patterns. Implements sophisticated monitoring with automatic metric collection and custom transformer support for pre/post processing.
Model Optimization Techniques
Model serving systems implement sophisticated optimization techniques including kernel fusion, mixed precision inference, and dynamic batching. They provide automated model quantization with INT8/FP16 support and hardware-specific optimizations. The systems include advanced features like model pruning, knowledge distillation, and architecture optimization. Features include efficient memory management with zero-copy inference and specialized attention computation. Implements sophisticated caching strategies for both model weights and inference results.