"Having it on‑prem is just a lot cheaper to train-and actually faster. Acres may be a small startup of only about 70 people, but it is one of a growing number of niche data companies quietly assembling GPU clusters outside the walls of Big Tech, in a bet that owning their own compute will be a competitive edge."
"This hardware can cost more than $25,000 per GPU, plus ongoing energy costs. During supply shortages like last year, it can be difficult for smaller companies to obtain them without months on waiting lists. But to run a geospatial data intelligence company, Malloy says having their own cluster just made more sense."
Acres, a 70-person geospatial data intelligence startup, is investing in on-premises GPU infrastructure to train machine learning models more cost-effectively than cloud services. Founder Carter Malloy purchased high-end NVIDIA GPUs and is installing direct cabling to connect them to his data science team's computers. This trend extends beyond Acres, with other niche data companies and venture firms like Andreessen Horowitz assembling their own GPU clusters. While hardware costs exceed $25,000 per GPU plus energy expenses, and supply shortages create acquisition challenges, owning compute infrastructure provides faster training and reduced operational costs. Malloy previously ran AcreTrader, a farmland investment platform, before pivoting to focus exclusively on data services.
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