Archive: https://archive.today/Wx9bg From the post: >>Why did I decide to invest in an on-prem, DIY server? For a few reasons: Unrestricted Learning: I'm fascinated by the whole AI stack--from the bare-metal, to low-level compilation, to high-level programming abstractions. I want to have unfettered access to all the hardware and software layers so I can follow my curiosity into the internals without the limitations you find with cloud-hosted instances. Hands-On Operations: Managing the server can be a hassle, but I do it for the operational experience. I'm treating my home server as a microcosm of a data center, implementing monitoring and logging systems, configuring backups, managing power and temperature—all things we take for granted with the cloud, but I strangely find interesting. Cost Savings: You pay a lot upfront for the hardware, but if your usage of the GPU is heavy, then you save a lot of money in the long run. I'm running a lot of AI workloads so the investment is probably justified, although time will tell. For low utilization rates, cloud instances are usually more cost-effective. (Tim Dettmers has a good breakdown of the tradeoffs.)