ML//AI infrastructure stack

The AI infrastructure stack is the chain that turns silicon and data into a usable model-powered system: accelerators, interconnect, drivers, kernels, runtimes, model representation, serving, orchestration, tools, permissions, observability and product interfaces.

The AI infrastructure stack is the chain that turns silicon and data into a usable model-powered system: accelerators, interconnect, drivers, kernels, runtimes, model representation, serving, orchestration, tools, permissions, observability and product interfaces.

Each layer changes the meaning of the next. Compression that no kernel exploits saves storage but not time. A capable model behind a poor tool interface becomes clumsy. Excellent agent planning with excessive credentials becomes a security problem. This is why applied AI engineering cannot stop at prompting or model choice.

The model may be the most discussed layer. The user experiences the multiplication of all of them.