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End-to-End AI Inference Architecture

  • AI inference is the process of running a trained model to generate predictions
  • Uses pre-trained weights to produce outputs from new inputs
  • The goal: deliver fast, reliable, and scalable responses
  • User-facing applications depend heavily on predictable low latency
  • Overall flow: load model → serve model → route requests → scale automatically → monitor performance
  • Represents the full lifecycle from model loading to production monitoring AI Inference Flow

Model Loading and Serving

  • Models are loaded into GPU/CPU memory using an inference server
  • Popular serving systems include:
    • NVIDIA Triton Inference Server : Multi-framework support with optimized scheduling
    • TorchServe : Good for PyTorch-based services
    • TensorFlow Serving : Highly optimized for TensorFlow models
    • Ollama, vLLM : optimized for LLMs, Excellent continuous batching and high throughput
  • Models can be optimized to reduce inference latency
    • ONNX, TensorRT, quantization, pruning, and graph optimizations
    • Reduces compute cost while maintaining accuracy

Inference Server, Ref.: https://developer.nvidia.com/dynamo-triton

Performance and Monitoring

  • Key inference metrics include:
  • latency – average & p99 latency for user experience
    • Lower latency → better UX, especially for LLMs & real-time apps
  • throughput – requests/second or tokens/second
    • Higher throughput → better hardware utilization
  • GPU utilization – measures processing efficiency
    • Prevents underuse or overload
  • memory usage – especially VRAM usage
    • Larger models require precise memory planning
  • Logs and metrics are collected using tools like:
  • Prometheus, Grafana, ELK
    • Provides dashboards to visualize real-time performance
    • Monitoring ensures reliable, stable inference service
    • Detects regression, degradation, or scaling issues early
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