Resources

AI infrastructure is the compute, storage, networking, serving and operations layer that production AI runs on. What it includes, and how it differs from a model, a platform and an application.

A reference architecture for enterprise AI: the layers a request passes through, what each one owns, the cross cutting concerns, and the decisions that vary by organization.

Most organizations end up running AI across more than one environment. What hybrid actually means, the four patterns that recur, and the data flow and latency questions that decide whether it works.

Many enterprise AI deployments need no accelerators at all. When a CPU is enough, when a hosted endpoint removes the question, and the four conditions that genuinely justify buying your own.

Designing and operating accelerator capacity for production AI: memory as the binding constraint, sharing and scheduling, utilization, power and cooling, and the lifecycle nobody budgets for.

Inference infrastructure answers requests against a model that already exists. Loading, memory, batching, concurrency, autoscaling and the latency behavior that makes it unlike web serving.

How model endpoints are structured in production: model servers, versioning and rollback, routing, health checking, multi model environments and the observability that makes changes safe.

Why AI workloads are containerized, what containers do and do not isolate, the image size and GPU access problems specific to AI, and the security boundary NIST is explicit about.

How Kubernetes schedules accelerators, why GPU requests behave unlike CPU, what autoscaling does and does not solve for inference, and when an orchestrator is more machinery than you need.

An AI gateway centralizes access to models: authentication, routing, rate limiting, logging and cost attribution. What it does, how it differs from an API gateway, and when it earns its place.