Inference Latency
Inference latency measures the delay between input and output in machine learning model predictions, affecting real-time usability and system responsiveness.
Inference latency is a core performance metric that quantifies the end-to-end time required for a model to process input data, perform computations, and return a prediction. It can be decomposed into queuing delay, compute time, and data transfer overhead. Factors influencing latency include model size and architecture, hardware accelerators, software runtime, batch size, data preprocessing and postprocessing, network latency, and serving stack. Techniques to reduce latency cover model optimization (pruning, quantization, distillation), compiler and runtime optimizations (operator fusion, graph optimizations), and hardware acceleration (GPUs, TPUs, NPUs). Real-time and near-real-time applications (autonomous systems, trading, interactive assistants) demand tight latency budgets and careful measurement of tail latency (e.g., p95/p99).
graph LR
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Rel_network_latency["network-latency"]:::related -.-> Center
click Rel_network_latency "/terms/network-latency"
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Inference latency is bounded by compute intensity (FLOPs) and memory bandwidth. Optimization involves operator fusion, constant folding, and precision reduction (Quantization). Tail latency (P99) is critical in distributed systems to prevent cascading timeouts. Benchmarking standards, notably MLPerf, provide comparative data across CPU, GPU, and ASIC architectures (TPUs, NPUs).