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Use Case #2

AI Infrastructure Cost Optimization

Increase GPU utilization from 40% to 75%+ through intelligent orchestration, deferring $8M+ in expansion CapEx per 100-node cluster annually.

75%
GPU Utilization
40%
Cost Reduction
$8M+
Annual CapEx Deferred

The Challenge

GPU clusters typically run at 40-50% utilization while consuming maximum power and cooling resources. Training workloads, inference jobs, and batch processing compete inefficiently, leading to:

  • Wasted capacity: Expensive GPUs sitting idle during off-peak hours or between workloads
  • Premature expansion: Organizations buy more hardware before optimizing what they have
  • High cost-per-inference: Training and inference workloads compete, driving up operational costs
  • Thermal inefficiency: Clusters run hot even at low utilization, maximizing cooling costs

In MENA regions, these challenges are amplified by extreme ambient temperatures, import constraints on high-end GPUs, and the strategic importance of AI sovereignty.

The Software Layer

Our AI infrastructure optimization layer orchestrates workloads intelligently across GPU clusters, balancing training, inference, and batch jobs to maximize utilization while respecting thermal and power constraints.

1

Dynamic Workload Orchestration

Intelligent scheduling between training, inference, and batch jobs. Fill idle GPU time with inference requests or batch workloads without impacting training SLAs.

2

Power-Aware Placement

Match workload intensity (training vs inference) to thermal capacity and power limits. Heavy workloads go to cool nodes; light jobs fill thermal headroom.

3

Multi-Tenant Optimization

Enable resource sharing across teams and projects without performance degradation. Increase cluster density while maintaining isolation guarantees.

4

Real-Time TCO Tracking

Continuous monitoring of cost-per-inference, utilization metrics, and economic efficiency. Optimization recommendations updated hourly.

The Numbers: GPU Cluster Transformation

Metric Before Optimization After Optimization Impact
GPU Utilization 40-45% average 75-80% average +35-40 points
Cost Per Inference $0.10 $0.06 -40%
Effective Cluster Capacity 100 nodes baseline ~170 nodes equivalent +70% capacity
Training Time (same model) Baseline 100% Maintained or improved No degradation
CapEx Expansion Deferred $12M planned (50 nodes) Deferred 18-24 months $8-10M deferred
Power & Cooling Efficiency Max consumption at 40% util Matched to actual load 15-20% OpEx reduction

*Based on 100-node GPU cluster (A100/H100 class). Actual results vary by workload mix and cluster configuration.

Our Pricing Model

Subscription Based on Node Count

$800-1,200
per GPU node/month (tiered pricing)
20-30%
of utilization gains value captured
60-90 days
Time to positive ROI

Example: 100-node cluster at $1,000/node/month = $1.2M annual fee. Saves $8M in deferred expansion + $1-2M in OpEx = 6-8x ROI.

What's Included

  • Continuous workload optimization
  • Real-time TCO dashboard
  • Dedicated support team
  • Quarterly optimization reviews

Performance Guarantee

We guarantee a minimum 25-point increase in GPU utilization within 90 days, or you pay nothing for the pilot period.

Training SLAs are contractually protected—no degradation in model training times.

Why This Matters for MENA AI Infrastructure

GPU Import Constraints

High-end GPUs (H100, A100) face export restrictions and long lead times. Maximizing utilization of existing clusters is strategically critical.

Impact: 35% utilization gain = deferring 35-node expansion for 18-24 months.

AI Sovereignty Goals

Regional AI capabilities must be built on locally controlled infrastructure. Optimization extends runway for sovereign AI initiatives.

Impact: More AI capacity from existing hardware = less dependency on external cloud providers.

Extreme Climate Reality

GPUs generate immense heat. In 50°C+ environments, cooling is the limiting factor. Thermal-aware optimization directly addresses this.

Impact: Power-aware placement reduces thermal hotspots, cutting cooling costs by 15-20%.

Economic Efficiency

Every AI initiative faces scrutiny on cost-per-inference and training economics. Optimization makes business cases stronger.

Impact: 40% reduction in cost-per-inference = more AI projects become financially viable.
"We went from 42% to 78% GPU utilization in 10 weeks. That's equivalent to adding 36 nodes without buying a single GPU. The cost-per-inference drop made three AI projects economically viable that we'd shelved. This is the difference between theory and execution."

— VP AI Infrastructure, Regional Tech Company

*100-node A100 cluster. Client name withheld per NDA.

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