Triton Inference Server
NVIDIA's open-source inference server for serving multiple ML models on GPU and CPU infrastructure with maximum performance.
NVIDIA Triton serves ML models from different frameworks simultaneously on GPUs with dynamic batching and maximum inference performance.
Explanation
Triton supports TensorRT, ONNX, PyTorch, TensorFlow, Python, and other backends simultaneously. Features include dynamic batching, model ensembles, concurrent model execution, and detailed performance monitoring.
Marketing Relevance
Triton is the industry standard for high-performance GPU-based model serving in data centers.
Common Pitfalls
Complex configuration for beginners. NVIDIA hardware dependency for GPU features. Model ensemble debugging.
Origin & History
NVIDIA released the TensorRT Inference Server in 2019, renamed to Triton Inference Server in 2020. Multi-framework support and model analyzer were added incrementally. Triton is now standard in cloud GPU deployments on AWS, GCP, and Azure.
Comparisons & Differences
Triton Inference Server vs. vLLM
vLLM specializes in LLM serving with PagedAttention; Triton is a general multi-framework inference server.
Triton Inference Server vs. BentoML
BentoML offers better developer experience and packaging; Triton offers superior GPU performance and hardware utilization.