Model Serving Architecture - Interactive Visualization

Production model serving is a layered system: requests pass through a load balancer, then an API gateway for auth and rate limiting, then a model server that batches requests and runs GPU inference. Each layer adds latency. Triton Inference Server is the fastest option - it supports TensorFlow, PyTorch, and ONNX models in a single server with dynamic batching. TorchServe handles PyTorch natively with custom handlers. KServe abstracts the serving runtime behind a Kubernetes CRD. Response caching can cut gateway latency by 65% for repeated queries. This interactive demo shows p50/p95/p99 latency breakdown at each stage.

• Triton: supports multi-framework models (TF, PyTorch, ONNX, TensorRT) in one server - lowest latency for GPU inference
• Dynamic batching: accumulate requests in a queue window (1-5ms) and process together - 4-8x throughput gain
• p99 latency: the 99th percentile matters most for user experience - one slow GPU kernel inflates it significantly
• Canary deployment: route 10% of traffic to new model version, monitor metrics, then promote or rollback
• Response cache (Redis): cache identical or similar requests - critical for embedding models serving repeated queries
• Load balancer health checks: remove unhealthy pods immediately - stale pods cause visible latency spikes

Part of the EngineersOfAI Interactive 3D - free interactive visualizations covering every major concept in machine learning and AI engineering. Hover any element for a plain-English explanation. No code required.