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AI containers are the path to fast, portable, secure model delivery: hardware-aware runtimes, pinned CUDA/ROCm stacks, and minimal images bring near-native performance from laptops to clusters. This guide shows how to go rootless, wire up NVIDIA/AMD GPUs, build lean multi-arch images, serve with vLLM/llama.cpp/Triton, secure with cosign, scale on Kubernetes—and previews OCI model artifacts, lazy weights, WASM/WebGPU, and microVM isolation.