A real-time inference engine is an optimised runtime that executes trained machine-learning models with low, predictable latency to serve predictions within interactive or streaming time budgets. It applies techniques such as operator fusion, quantisation, batching, and hardware acceleration to meet throughput and latency targets. It is the serving layer that turns offline-trained models into responsive online services.
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- Engines optimise the inference graph through kernel fusion, quantisation (INT8/FP16), and dynamic batching, mapping operators onto CPUs, GPUs, or accelerators. Serving infrastructure adds autoscaling, model versioning, and request scheduling to sustain tail-latency SLAs under variable load while keeping per-request cost low.