TL;DR: Sovereign AI deployment requires careful planning across hardware, facilities, networking, and operations. Typical deployment takes 12-20 weeks. Start with a pilot (4-8 GPUs, $140-380K Year 1), learn, then scale.
Prerequisites
Before diving into sovereign AI deployment, ensure you have:
Step 1: Assess Your Workloads
Not all AI workloads are created equal. Start by categorizing:
| Workload Type |
Key Metrics to Capture |
| Training |
Model size (parameters), dataset size, training frequency, accuracy requirements |
| Inference |
Requests per second, latency requirements, model size, batch size capabilities |
Step 2: Choose Your Hardware
For Training-Heavy Workloads
| Component |
Recommendation |
| GPUs |
NVIDIA H100 or H200 for large models |
| Interconnect |
Multi-node clusters with NVLink/InfiniBand |
| Storage |
High-speed NVMe arrays |
For Inference-Heavy Workloads
| Component |
Recommendation |
| GPUs |
Consider memory requirements carefully |
| Evaluation |
Cost-per-inference metrics |
| Architecture |
Plan for redundancy and scaling |
Step 3: Design Your Infrastructure
Power Requirements
| Factor |
Guidance |
| Base calculation |
GPU TDP + 40-50% overhead |
| Redundancy |
Ensure redundant power feeds |
| Example |
8× H100 (700W each) = 5.6kW GPU + 2.8kW overhead = ~8.4kW total |
Cooling Requirements
| Power Density |
Cooling Solution |
| Up to ~30kW per rack |
Air cooling works |
| Above 30kW per rack |
Liquid cooling required |
| Maximum density |
Direct-to-chip cooling |
Networking Requirements
| Network Type |
Minimum Spec |
| Multi-node training |
InfiniBand (400Gbps+) |
| Storage network |
100GbE minimum |
| Management |
Separate isolated network |
Step 4: Deploy and Test
| Phase |
What To Do |
| Burn-in testing |
Run stress tests for 72+ hours |
| Benchmark |
Establish baseline performance metrics |
| Validate |
Test with actual workloads before production |
| Document |
Create runbooks for operations team |
Step 5: Operationalize
Infrastructure Planning Checklist
Hardware Planning
Facility Requirements
Network Architecture
Storage Planning
Software Stack
Phased Deployment Approach
Phase 1: Foundation (Weeks 1-8)
Objective: Get basic infrastructure operational
| Activity |
Details |
| 1 |
Finalize hardware orders and confirm delivery dates |
| 2 |
Prepare colocation space (power, cooling, racks) |
| 3 |
Provision network connectivity |
| 4 |
Set up management infrastructure (jump hosts, monitoring) |
| 5 |
Document procedures and access controls |
Deliverable: Empty racks ready for hardware
Phase 2: Hardware Deployment (Weeks 6-12, overlapping)
Objective: Install and validate hardware
| Activity |
Details |
| 1 |
Receive and inventory hardware |
| 2 |
Rack and cable servers |
| 3 |
Configure BMC/IPMI management |
| 4 |
Run burn-in tests (72+ hours) |
| 5 |
Validate GPU functionality |
| 6 |
Performance benchmark against specifications |
Deliverable: Hardware operational, benchmarks documented
Phase 3: Software Stack (Weeks 10-14, overlapping)
Objective: Deploy platform software
| Activity |
Details |
| 1 |
Install and configure OS |
| 2 |
Deploy container runtime |
| 3 |
Configure orchestration (if using) |
| 4 |
Install NVIDIA drivers and CUDA |
| 5 |
Deploy ML frameworks |
| 6 |
Set up monitoring and alerting |
Deliverable: Platform ready for workloads
Phase 4: Workload Migration (Weeks 12-20)
Objective: Migrate production workloads
| Activity |
Details |
| 1 |
Port workloads from development/cloud |
| 2 |
Validate model accuracy |
| 3 |
Performance tune for new hardware |
| 4 |
Establish CI/CD pipelines |
| 5 |
Documentation and runbook creation |
| 6 |
Team training |
Deliverable: Production workloads running
Common Mistakes and How to Avoid Them
Mistake 1: Under-sizing Storage
| Problem |
Solution |
| AI workloads generate massive datasets, checkpoints, and logs. Teams frequently underestimate by 5-10x. |
Plan for 10TB minimum per GPU for active storage. Add archival tier for historical data. Budget for 2x expected needs. |
Mistake 2: Ignoring Network Bottlenecks
| Problem |
Solution |
| GPU compute is fast; storage and network are often the bottleneck. |
Minimum 100GbE for storage network. InfiniBand (400Gbps+) for multi-node training. Dedicated management network. Monitor from day one. |
Mistake 3: Skipping Burn-in Testing
| Problem |
Solution |
| Hardware failures are most common in first 72 hours. Shipping into production invites downtime. |
Run stress tests for 72+ hours before production. Exercise GPUs at full load. Monitor for thermal throttling, memory errors, component failures. |
Mistake 4: Over-provisioning Initially
| Problem |
Solution |
| Teams buy for projected 3-year needs, pay to power/cool unused capacity. |
Buy for 12-18 month needs. Plan infrastructure (power, cooling, space) for 3-year growth. Add hardware as demand materializes. |
Mistake 5: Underestimating Operational Complexity
| Problem |
Solution |
| Running GPU clusters requires different skills than traditional IT. |
Train team on GPU-specific operations. Document procedures. Consider managed services. Budget 0.25-0.5 FTE per rack for operations. |
Frequently Asked Questions
How much should we budget for initial deployment?
| Deployment Size |
Hardware |
Year 1 OpEx |
Total Year 1 |
| Pilot (4 GPU) |
$100-150K |
$40-60K |
$140-210K |
| Small (8 GPU) |
$200-300K |
$60-80K |
$260-380K |
| Medium (32 GPU) |
$800K-1.2M |
$150-200K |
$950K-1.4M |
| Large (128 GPU) |
$3-5M |
$400-600K |
$3.4-5.6M |
OpEx includes colocation, power, support, and partial FTE for operations.
Should we hire in-house or use managed services?
| Choose In-House If... |
Choose Managed Services If... |
| AI infrastructure is core to your business |
AI is important but not core |
| You have 50+ GPUs justifying dedicated team |
You have <50 GPUs |
| You need rapid customization and control |
You need faster time to production |
| You can attract and retain GPU ops talent |
You want to focus engineering on AI, not infrastructure |
Pro tip: Many organizations use a hybrid: managed services for infrastructure operations, in-house team for workload optimization.
What's the minimum viable deployment?
For production inference:
| Component |
Specification |
| Servers |
2 GPU servers (primary + redundant) |
| Network |
100GbE |
| Storage |
10TB NVMe |
| Facility |
Colocation with liquid cooling capability |
| Monitoring |
Full alerting stack |
| Budget |
~$300-400K hardware, ~$80-100K/year operations |
For production training:
| Component |
Specification |
| Servers |
4-8 GPU servers (depends on model size) |
| Interconnect |
InfiniBand |
| Storage |
50TB+ high-speed |
| Management |
Dedicated cluster management |
| Budget |
~$1-2M hardware, ~$200-300K/year operations |
Conclusion
Sovereign AI infrastructure requires upfront planning but delivers long-term advantages in cost, control, and capability. Start with a well-defined pilot project, learn from the deployment, and scale from there.