What's ready, what's not, where the boundary lives.
Hands-on technical validation across the 4+1 AI Infrastructure framework. A lab takes a slice of the stack, a single layer, a combination, or the whole model, builds it for real, and publishes where the seams actually fall, scored against the 4+1 model and DAPM.
Labs
Borrow the vendor’s plumbing, not its judgment
I built a retrieval pipeline across a public-cloud data plane and a local box, the compute I keep below the cloud’s managed abstraction, to map where authority actually sits across the 4+1 stack. The economics were the boring part: eighty-four cents, the cloud faster. The finding worth keeping is what the managed path quietly decides for you, and the two questions the lab now knows to ask.
Own the weights, or the platform owns you
Lab one found the Spark loses to the cloud on inference. That verdict held only for commodity base models. The moment you need a custom model you own, the cloud stops selling tokens and starts renting you floors, and the managed path takes something you cannot get back: the weights. Throughout, the box means the compute, serving, and training you keep below the platform’s abstraction instead of ceding them, and the NVIDIA DGX Spark is where this lab draws that line.