Serving machine learning models

Inference is GPU-bound and slow per request, so capacity is planned in requests per GPU. Dynamic batching, response caching, smaller models and autoscaling on queue depth keep latency and cost under control.

Where it shows up in real systems

  • Model inference service

    Each request takes tens to hundreds of milliseconds of GPU time, so a few hundred requests per second already need several expensive GPUs. Batching requests, caching answers and choosing the right accelerator change the bill by an order of magnitude.

    In production: NVIDIA Triton, vLLM, Amazon SageMaker endpoints, Google Vertex AI, KServe

See it break, then fix it

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