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Model Serving Architecture

Definition

Infrastructure for serving ML models in production at scale — handling concurrent requests, GPU management, batching, and auto-scaling.

Serving Stack

graph TD
    A[API Gateway<br/>Rate limiting, auth] --> B[Load Balancer]
    B --> C[Model Server Pool]

    C --> C1["Triton Inference Server<br/>(GPU, multi-model)"]
    C --> C2["TorchServe<br/>(PyTorch models)"]
    C --> C3["ONNX Runtime Server<br/>(Cross-framework)"]

    C1 --> D[GPU Pool<br/>Auto-scaling based on queue]

    style C1 fill:#4051B5,color:#fff

Serving Frameworks

Framework Key Feature Best For
Triton (NVIDIA) Multi-model, multi-framework, dynamic batching Production GPU serving
TorchServe PyTorch-native, easy deployment PyTorch models
TF Serving TensorFlow-native, high performance TensorFlow models
Ray Serve Python-native, easy scaling Complex pipelines
BentoML Model packaging + serving Rapid deployment

Key Optimizations

Optimization Impact
Dynamic batching Batch multiple requests → 2-8x throughput
Model concurrency Run multiple models on same GPU
Model caching Keep hot models in GPU memory
Auto-scaling Scale GPU instances based on request queue depth
A/B model routing Route traffic between model versions

Key Takeaways

Summary

  • Triton Inference Server is the industry standard for GPU model serving
  • Dynamic batching is the biggest throughput optimization — batches individual requests automatically
  • Auto-scaling based on queue depth handles traffic spikes
  • Multiple eKYC models (liveness, recognition, OCR) can share GPU resources via Triton