Inference infrastructure is the stack of hardware, serving software, and orchestration used to deploy machine-learning models for low-latency, high-throughput prediction in production. It encompasses accelerator fleets, model servers, autoscaling, load balancing, batching engines, and caching layers that route requests and manage GPU memory. It is what makes real-time AI services such as search and chat economically and operationally viable at scale.
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- Key components include continuous-batching servers, paged attention for KV-cache management, autoscalers, and request routers. Operators optimise for the trade-off between latency, throughput, and cost-per-token under fluctuating demand.