Case study · Education and Research
90% GPU utilization,faster fine-tuning.
A large research-intensive academic and innovation institution supporting advanced scientific research, industry collaboration and AI-driven digital transformation. Its environment spans large-scale model training, fine-tuning, evaluation and applied AI research, and needs high-performance computing, flexible scheduling and strong operational control across heterogeneous infrastructure. As demand for AI and large language model research accelerated, traditional computing environments could not deliver the utilization, efficiency or scale the research required.
Constraints and answers
Research moves at the speed of whatever the scheduler allows.
The platform that changed what the scheduler allows
Data, hardware and coordination slowed training
AI model training was held back by data preparation, algorithm complexity, hardware constraints and cross-team coordination.
Fine-tuning constrained by compute and compatibility
Model fine-tuning was constrained by limited computing resources, parameter selection, overfitting risk and architectural compatibility.
Model evaluation resource-intensive and hard to scale
Evaluating models required extensive benchmark selection, data processing and interpretation, making long-term evaluation resource-intensive and hard to scale.
Mixed computing environments were inefficient to run
Heterogeneous computing environments introduced inefficiencies in scheduling, utilization and operational management.
Low GPU utilization limited the return
Low overall GPU utilization limited the return on high-performance computing infrastructure.
End-to-end AI platform across the full model lifecycle
A unified AI platform covering model training, fine-tuning, evaluation and application scenarios, with optimized model packages to improve development efficiency.
Intelligent scheduling and resource pooling
A cloud-native computing scheduling platform improved resource pooling, optimization, operation and on-demand allocation, answering low utilization and scheduling inefficiency directly.
Heterogeneous computing power management
Unified management and scheduling across heterogeneous GPU resources, spanning accelerators from more than one supplier, reduced supply risk while keeping autonomy and control over future computing infrastructure.
Cloud-native AI operations and observability
Standardized workflows and operational visibility improved coordination, reduced manual overhead, and accelerated AI experimentation and deployment.
Training, fine-tuning and evaluation now share one scheduler and one pool of cards.
The results
90% GPU utilization across 500 cards, fine-tuning 60% faster.
- 90%
- GPU UTILIZATION ACHIEVED
- A step change in the efficiency of computing resource usage.
- 500
- GPU SCALE SUPPORTED
- Large-scale AI training and fine-tuning workloads carried on one platform.
- 60%
- BETTER FINE-TUNING EFFICIENCY
- Faster experimentation cycles and shorter time to results.
- AI LIFECYCLE COVERAGE
- Training, fine-tuning, evaluation and application on one platform.
The same hardware, scheduled properly, is a different institution.
The stack
What was deployed
INTELLIGENCE
Sovereign AI Platform
AI workload orchestration, model scheduling and optimization, supporting efficient training, fine-tuning and deployment of large-scale AI models.
PLATFORM
Sovereign Cloud Platform
The cloud-native operating platform for workflow orchestration, resource management, observability and lifecycle governance across AI research.
INFRASTRUCTURE
Sovereign Virtualization
The heterogeneous compute, storage and networking infrastructure for large-scale GPU clusters, resource pooling, and high-performance AI workloads.
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