Tell us the GPUs you need — for example, 128 B300 — and we deliver a fully production-ready AI compute environment. We select the optimal data-center or colocation site, design a custom cluster architecture, procure the specialized equipment, and install and connect the hardware. You get a working cluster without having to assemble the vendors, logistics, and expertise yourself.
Keeping a GPU cluster healthy takes constant attention — when a machine fails or misbehaves during training, someone has to diagnose and fix it fast. We provide that always-on operational support for any cluster, whether or not we built it. It's the kind of dedicated infrastructure team large companies staff internally; we bring the same capability to small and midsize teams, so a hardware failure doesn't become your engineers' problem to solve.
We help you get more out of the hardware you have, improving the efficiency of both training and inference so your compute goes further.
Building and running AI infrastructure is hard, expensive, and unforgiving of mistakes. Our founders bring direct, hands-on experience delivering exactly this at scale — turnkey GPU cluster deployments across 6 customers and 9 sites, totaling ~12MW of capacity and ~800 B300 servers. We pair that experience with established supplier relationships, so your deployment is faster, smoother, and more reliable.
The same depth carries into operations and efficiency. Our team has run data center operations for an AI inference cloud, backed by 15 years of keeping mission-critical infrastructure running, and brings over a decade of optimizing large-scale ML training and inference — so your cluster stays healthy and your compute goes further.
Spent 11 years at Meta building and optimizing the recommender systems and infrastructure behind Ads, News Feed, Stories, and Instagram Reels, managing CPU/GPU compute capacity for large-scale ML systems. Worked as both an ML engineer and an engineering manager.
Has led turnkey GPU cluster deployment, data center operations, and capacity expansion at an AI inference cloud startup. Brings 15 years of infrastructure deployment and operations experience across banking, semiconductor manufacturing, video streaming, and fintech. Worked as both an infrastructure engineer and a DevOps manager, leading teams, vendors, incident response, and complex multi-site deployments.
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