AI inference explained: The hidden process behind every prediction

This post provides a foundational explanation of AI inference, detailing its two stages (training and inference), the request lifecycle, and the inherent challenges of balancing speed, reliability, and cost. It introduces key concepts like inference frameworks (TensorRT-LLM, SGLang, vLLM) and infrastructure optimizations (intelligent routing, geo-aware load balancing, SLA-aware autoscaling). It also defines critical metrics for measuring inference success: latency (time to first token, total generation time), throughput (tokens per second, requests per second), and cost, emphasizing strategies like batching.