Case study · Cloud Services
GPU as a service,sold by the hour.
A cloud service provider running fully self-managed, sovereign compute centers that deliver public, private, hybrid and dedicated cloud services. Its infrastructure carries large-scale AI training, inference and fine-tuning, and needs high-performance compute, flexible resource allocation and commercial-grade operations across several customer segments. As demand for AI computing grew, traditional GPU management could not support multi-tenant usage, fair allocation, billing transparency or high utilization.
Constraints and answers
A GPU you cannot meter is a GPU you cannot sell.
The platform that made every GPU hour sellable
AI workloads demanding high-performance compute
AI workloads required a high-performance compute environment supporting training, inference and fine-tuning at scale.
Too few sales and allocation models
Traditional GPU management lacked the flexibility to support several sales and allocation models for diverse customer needs.
Idle GPUs eroding return on infrastructure
Low utilization and inefficient scheduling reduced the return on GPU infrastructure.
Metering too coarse for effective commercialization
Without fine-grained metering and billing, GPU services could not be commercialized effectively.
Mixed workloads needing framework and model compatibility
Heterogeneous AI workloads required strong compatibility with mainstream frameworks and large models.
Cloud-native GPU management and scheduling
A GPU operations platform with full lifecycle control of GPU resources, including real-time monitoring, adaptive scheduling and optimized allocation across tenants and workloads.
Self-service onboarding and multi-tenant operations
Users register, access and manage compute resources on their own, which is what makes multi-tenant GPU service delivery scale.
Flexible GPU sales and allocation models
The platform supports shared, exclusive and quota-based allocation modes, so offerings can be tailored to different customer requirements.
Integrated metering, billing and analytics
Fine-grained resource measurement, billing and analytics make the GPU service transparent and commercially operable.
Users register and manage their own compute, and the platform counts every hour of it.
The results
80% GPU utilization, and every hour accounted for.
- 35%
- SCHEDULING EFFICIENCY
- On-demand scheduling raised GPU utilization.
- 80%
- GPU UTILIZATION ACHIEVED
- Fine-grained management cut idle capacity.
- 5
- DATA ACCESS PERFORMANCE GAIN
- Storage optimization sped up throughput.
- 40%
- NETWORK THROUGHPUT GAIN
- High-speed networking for data-heavy work.
- 50%
- INFERENCE PERFORMANCE GAIN
- Inference ran efficiently across model versions.
Utilization is the margin. Everything else on this page serves it.
The stack
What was deployed
INTELLIGENCE
Sovereign AI Platform
GPU scheduling, resource pooling, model services and AI workload orchestration to support commercial AI services.
PLATFORM
Sovereign Cloud Platform
Cloud-native platform services for multi-tenant operations, observability and lifecycle management across AI workloads.
INFRASTRUCTURE
Sovereign Virtualization
The elastic compute, high-performance storage and networking required for GPU-as-a-service delivery at scale.
역량에 관한 노트
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