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NVIDIA TRITON INFERENCE SERVER
Standardized, optimized inference serving for any model on GPU or CPU
BSD-3-Clause
ABOUT
Deploying multiple models from different frameworks (TensorFlow, PyTorch, ONNX) on GPU infrastructure requires separate serving stacks per framework, wasting engineering effort and GPU memory. Triton solves this by providing a unified serving runtime that handles models from any framework with concurrent model management, dynamic batching for GPU utilization, model pipelines for preprocessing/postprocessing, and production features like health endpoints and metrics.
INTEGRATION GUIDE
1. Serve multiple models from different frameworks on shared GPU infrastructure concurrently
2. Optimize GPU utilization with dynamic batching and concurrent inference execution
3. Build inference pipelines with pre-processing, post-processing, and ensemble models
4. Deploy models at scale in production environments with health checks and Prometheus metrics
5. Serve LLMs with optimized TensorRT-LLM backend for low-latency generation
TAGS
inference-servernvidiamodel-servinggpudeep-learningmlopscloudedgeproduction