spacex mentioned in their latest earnings call that they’re investigating modular inference clusters. the mechanical engineering behind warehousing compute is already hard, so why would they go through the effort of making the challenge more difficult by making their compute modular (portable and sized for transportation)? 1. electricity cost: as an example, electricity prices can vary by 10x within texas alone. being able to pick up your compute and move it allows you to take advantage of the electricity market. ai inference is a rare commodity where supply is geographically disconnected from demand. you can take advantage of dirt-cheap electricity and still serve compute to high-cost-of-living markets like nyc with negligible performance impact (latency) 2. labor: one of the factors going into the placement decision is whether or not you can staff the facility. logistics companies like ups, fedex, and walmart fight with this constantly. it's a large factor in deciding where a new facility will be located 3. upgradability: a huge part of designing these facilities is planning for future expansion or upgrades. modularity lets you define standard interfaces, which makes ripping out and replacing "units" of the warehouse easier than treating it like a brownfield project to upgrade. it's extra work on the front end to define and standardize these interfaces, but doing so pays off in the future 4. cost and scale: it's cheaper and faster to scale from 1x to 1,000,000x with a modular design by just connecting units together rather than designing for your target scale through greenfield or brownfield development. you also get economies of scale (wright's law) with this method. remember: there will be factories producing these "units". need more compute? stamp out more units. no need to customize. interestingly, this also reduces risk by pushing capex commitments further down the line when more information is available. if ai is what we think it is, just make more units. if it isn't, just make fewer 5. performance: the primary limiting mode of heat transfer in these systems, regardless of whether they are open-loop or closed-loop, is convection, not conduction or radiation: qDot = hA(T_out-T_in) qDot is your heat transfer rate (roughly, how much power it takes to cool the system) h will always be the same (with fluctuations depending on external factors like humidity that you can't really control) A (area) you can't change -- it's effectively your "fin," which is set and T_out is effectively the surface temperature of the chips you're trying to cool this leaves T_in -- which, for closed-loop systems, is roughly geographically independent. but for open-loop systems... anyways, all of this comes together to make sense. interestingly, tinycorp is doing this too with their exabox. tiny has a lot of engineering challenges with their thermals and have real challenges with 4 and 5, but spacex will be able to