130–240 kW per rack with NVIDIA H100/Blackwell, liquid-cooled for maximum AI performance per watt.
AI Infrastructure Solutions
AI Factory
One Site · Maximum Performance
"Deploy 512–10,000+ NVIDIA GPUs in a single data center."
Best for: Foundation model training, LLMs, diffusion & multi-modal models
AI Factory
Many Sites · Unified Control
"Deploy GPU clusters across multiple locations with federated learning."
Best for: Edge AI, regulated industries, multi-national enterprises
Built for centralized, hyperscale AI performance
Engineered for low-latency, sovereign, edge-driven AI
Supports CapEx and OpEx models aligned with enterprise financial strategies
Curated architecture enables faster deployment of production-ready AI environments
Accelerates time-to-production with standardized yet customizable AI infrastructure frameworks
Combines infrastructure, platform, observability, automation, and orchestration layers seamlessly
Enables real-time monitoring, insights, and performance tracking across AI operations
Reduces manual intervention through intelligent workflows and agentic orchestration capabilities
Maximizes compute efficiency for large-scale training and real-time inference workloads
Minimizes token and inference costs through intelligent workload and resource tuning
Ensures faster response times with optimized networking and GPU utilization strategies
Infrastructure customized specifically to meet unique AI and ML requirements
Enables scalable, high-performance inference environments for enterprise AI applications
Dynamically scales resources based on workload demand and performance requirements
Compatible with L40s, B200s, B300s across NVIDIA and AMD ecosystems
Includes compute, storage, backup, and disaster recovery for reliability
Deploy in private cloud or data center environments with full control
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AI innovation is accelerating rapidly, but the infrastructure required to run it at scale is struggling to keep pace, ultimately decelerating enterprises from operationalizing AI. Additionally, increasing infrastructure demands driven by data-sensitive use cases…
