power

Start from the power envelope, because it decides the GPU count more often than the budget does. The scalable unit that makes the design a copy of a known-good one, the four networks, and the three line items that gate the schedule while everyone is discussing GPUs.

Four separate facility limits have to be cleared and total building capacity is not one of them. Power per rack, heat rejection, floor loading and network reach each fail independently, and each has a different remedy with a different lead time. The options that exist, priced, and the one that is usually taken.

The GPU board power is about 60 percent of the answer. The rest is CPUs, memory, NICs, fans, switches, thousands of transceivers and the cooling plant, and each is a term people leave out. The full derivation, the term that surprises everyone, and the contracted number that is larger than all of it.

A training cluster is a synchronized load, so the peak is far above the average and the average is what everyone budgeted. The immediate mitigation takes minutes and costs a few percent of throughput, and the four durable fixes trade cost against throughput in different places.

A fixed power envelope turns hardware selection into an optimization with one objective and several overheads competing for the same watts. Where the 1 MW actually goes, why the answer is not simply the most efficient GPU, and the two overheads that can be cut to buy more compute.

Doubling per-rack density is four separate upgrades with different lead times, and the electrical one usually cannot be done while the hall runs. The phasing that keeps capacity available through it, the constraint that decides whether it is possible at all, and the number that says whether to upgrade or move.

A whole rack failing at once is a shared dependency, which narrows the causes to four before anyone touches anything. What the out-of-band network tells you in the first minute, why the order of restoration matters, and the decision about liquid cooling that has to be made before power returns.

700 W per GPU is the number everyone knows and it is only 40% of the answer. The chain from GPU TDP to node, to IT load, to facility power with PUE: about 1.8 MW, and what that means in racks and dollars.

The device has stopped answering on PCIe, so the driver cannot reset it and no software fix exists: the host has to re-enumerate the bus. What causes it, why the node is out until it reboots, the three-strike policy that separates a transient from a dying card, and the automation that handles it without a human.

Ten thousand GPUs is 1,280 nodes, ten pods, about 700 switches, a filesystem that swallows a 5 TB checkpoint in under a minute and a 14 MW feed. The bill of materials with a reason on every line, from the FLOPs the model needs to the spare pool the failure rate demands.

This is a test of whether you have a position you can defend, not of which problem you pick. What makes a thesis defensible, two worked examples with the arithmetic behind them, and the counterargument you have to be able to state before the interviewer does.

Four claims that are defensible from arithmetic available today, each with the counterargument that could sink it, and what each one implies about the work. Dated to 2026, because a thesis with no date is not a prediction.