AI is Transforming Mission-Critical Operations
Modern AI demands more compute, more cooling, and more licenses—driving costs out of control. To unlock AI’s potential without ballooning your budget, infrastructure consolidation is essential.
How much is AI costing your agency?
| Metric | Before | After (7:1 Consolidation) | Improvement | % Reduction |
|---|---|---|---|---|
| Physical Servers | ||||
| Annual Power + Cooling Cost ($) | ||||
| Annual Licensing Cost ($) | ||||
| Data Center Floor Space (sq. ft.) | ||||
| AI Model Training Time | ~7.0 hrs/model | ~1.5 hrs/model | 5.5 hrs faster | 78% |
AI is Transforming Mission-Critical Operations
But... modern AI demands more compute, more cooling, and more licenses—driving costs out of control. To unlock AI’s potential without ballooning your budget and wasting resources, infrastructure consolidation is essential.
Reality check: Federal data centers weren’t designed for the scale of today’s AI workloads
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Let's talk about how Wildflower can help you save.
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You've calculated the savings, now let's unlock them.
You've already seen how much your agency can save by consolidating servers and modernizing infrastructure. Now, take the next step: sit down with our Al Infrastructure Experts for a no-cost call designed specifically for federal agencies.
What to Expect:
- Analysis of your current infrastructure and projected cost savings
- Expert recommendations on server consolidation strategies that align with your mission
- Guidance on procurement options and available contract vehicles
- Clarity on performance optimization for today's Al workloads
Metrics We Use for the Wildflower Cost Consolidation Calculator
Industry Benchmarks
The consolidation rate (7:1) and resulting improvements draw from real-world examples seen across federal and enterprise IT modernization projects using:- Hyperconverged Infrastructure (HCI) platforms (eg, Nutanix, VxRail)
- Virtualization and containerization best practices (e.g., VMware, Red Hat OpenShift)
- GPU-accelerated servers for Al workloads
Key supporting sources include:
- IDC and Gartner reports on data center modernization and virtualization
- DoD and GSA case studies (e.g, MilCloud 2.0, DISA infrastructure reports)
- Vendor-published white papers from Dell, HPE, and NVIDIA that show real consolidation and cost savings metrics
Assumptions Used to Calculate the Metrics
Here’s how each "after" metric was estimated:
Metric
Assumption
Physical Servers
Based on a 7:1 consolidation ratio across virtualized workloads. This is achievable when migrating from legacy servers (~5–7 years old) to modern 2U/4U servers with dense CPU/GPU capacity.
Power & Cooling Costs
Average power draw per server: ~500W for older hardware, ~300W for newer HCI systems. Multiply by server count, convert to kWh, apply $0.10–0.12/kWh + HVAC overhead.
AI Model Training Time
Estimated from benchmarks showing 5–10x performance increase moving from CPU-only to GPU-enabled compute (e.g., NVIDIA A100 or H100). Actual speedup depends on model complexity.
Floor Space
Based on standard rack density: ~42U racks, 10–12 servers per rack. Reduction in footprint scales with server count and cooling requirements
Licensing Fees
Assumes licenses (OS, virtualization, monitoring, security) are tied to server count or core count. Rationalization follows server reduction.
Reality Check: What Can Vary?
- Consolidation ratios depend on current server utilization (many federal servers operate at 10-20%).
- Power savings may vary based on facility efficiency (PUE) and workload type.
- Al performance gains are very workload-dependent. Training vision models = big gains, inference on tabular data = less.
- Licensing structures vary widely between agencies and vendors.