Tell us the GPUs you need — for example, 144 B300 servers — and we handle everything from site selection and cluster architecture to procurement and installation, delivering a fully production-ready AI compute environment.
Once live, we keep it healthy: our always-on team diagnoses and fixes failures fast, so they never land on your engineers — for any cluster, whether or not we built it.
Leveraging our team's experience optimizing AI infrastructure at hyper-scale, we resolve bottlenecks and elevate training or inference GPU efficiency to get more out of the hardware you already have — a well-optimized system can achieve a 2X efficiency gain.
Building and running AI infrastructure is hard, expensive, and unforgiving of mistakes. Our founders have delivered exactly this at hyper-scale, and we pair that experience with established supplier relationships — so your deployment is faster, smoother, and more reliable.
Has deployed GPU infrastructure at hyper-scale. At a leading AI inference cloud provider, he single-handedly delivered ~30MW and ~2,000 B300 servers across 20 enterprise customers and 17 sites, representing billions of dollars in total infrastructure value.
Our founders bring years of experience optimizing large-scale AI systems — expertise we put to work for you: diagnosing your system, finding the bottlenecks, and lifting the GPU efficiency of your training or inference.
Led optimization projects that more than doubled the inference GPU efficiency of a flagship AI system at Meta. These cutting-edge optimization techniques were adopted across the company's core production fleet, saving billions of dollars in GPU expenditure.
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