Case study · AI Infrastructure
Ultra-high-densitycomputing power.
A large-scale computing infrastructure operator delivering next-generation high-density capacity for advanced AI workloads. The platform carries massive training and inference demand under strict requirements for stability, efficiency and hardware compatibility, inside real constraints on power, cooling and chip heterogeneity. As demand for large-scale AI computing accelerated, conventional data center architectures could not provide the density, efficiency or manageability the work needed.
Constraints and answers
Density is a power problem, a cooling problem, and a scheduling problem.
The architecture that answered them
Conventional architectures could not deliver density
Conventional data center architectures could not deliver sufficient computing density to meet large-scale AI training demands.
Power and thermal limits on scaling
Power consumption and thermal constraints limited scalability and operational efficiency in high-density environments.
Heterogeneous GPU environments complicated scheduling
Heterogeneous GPU environments introduced compatibility, scheduling and utilization challenges.
Traditional cluster management slowed time to value
Traditional cluster management and AI deployment processes increased complexity and slowed time to value.
Utilization at scale needed tighter integration
High utilization and low latency at scale required tighter integration between hardware, networking, storage and software.
Integrated hardware and software architecture
A coordinated management, computing and storage networking architecture balanced cost, performance and scalability while holding reliability, low latency, strong isolation and efficient expansion.
Ultra-high-density supernode framework
A supernode architecture combining multiple servers and GPUs with high-performance distributed storage and RDMA observability, built to maximize GPU efficiency and work past chip-level limits.
Advanced liquid-cooling design
High-density liquid-cooled micro-modules and in-floor liquid cooling pipelines support sustained high-performance operation under extreme compute density.
Cloud-native AI platform and cluster management
Cloud-native cluster management and optimized AI frameworks simplified deployment, improved scheduling, and accelerated AI application development and adoption.
Supernodes and liquid cooling push past the chip-level limit on density.
The results
128 cards to a micro-module, four groups fully interconnected.
- 128-card
- LIQUID-COOLED CLUSTER MICRO-MODULES
- Extreme compute density inside a compact footprint.
- 4
- INTERCONNECTED COMPUTING POWER GROUPS
- Large-scale unified computing capacity at low latency.
- GPU UTILIZATION AND EFFICIENCY
- Supernode architecture and RDMA observability raised effective GPU usage.
- AI DEPLOYMENT AND OPERATIONS
- Cloud-native management reduced complexity and shortened deployment cycles.
Hardware, cooling and software built as one platform, not three procurements.
The stack
What was deployed
INTELLIGENCE
Sovereign AI Platform
Supports AI workload orchestration, optimized scheduling, and high-efficiency utilization of heterogeneous GPU resources for large-scale training and inference.
PLATFORM
Sovereign Cloud Platform
The cloud-native operating platform for cluster management, observability, AI framework integration and lifecycle management at extreme density.
INFRASTRUCTURE
Sovereign Virtualization
Delivers the compute, storage and network infrastructure optimized for high-density deployment, liquid cooling integration, and large-scale AI workloads.
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