On-Premises vs. Cloud AI Infrastructure
| Criterion | On-Premises | Cloud |
|---|---|---|
| Cost (sustained workload) | Lower TCO over 3–5 years at 40%+ utilization | Higher TCO at sustained utilization; lower for variable workloads |
| Data sovereignty | Full control, data never leaves your facility | Data processed in provider infrastructure; jurisdiction varies |
| Compliance | Easier to document and audit | Provider compliance certifications may not satisfy all requirements |
| Performance | Consistent, predictable performance | Variable, depends on instance availability and network conditions |
| Lead time | 12–24 months for purpose-built facility | Minutes to hours for on-demand instances |
| Scalability | Requires planning and procurement | Elastic, scale up or down on demand |
| Operational burden | Internal team or managed services required | Provider manages infrastructure; you manage workloads |
| Vendor dependency | Hardware vendor dependency; operational independence | Deep dependency on cloud provider pricing and availability |
The right answer for most enterprises
Most large enterprises use a hybrid model: cloud for variable workloads, prototyping, and burst capacity; on-premises for sustained, high-utilization workloads with sensitive data. The question is not which is better, it is which is right for each workload.
GPU Platform Comparison
| Platform | GPU Memory | Training Performance | Inference Performance | Power | Best For |
|---|---|---|---|---|---|
| NVIDIA H100 SXM5 | 80 GB HBM3 | Highest (NVLink 4.0) | High | 700W TDP | Large model training, frontier AI |
| NVIDIA H100 PCIe | 80 GB HBM3 | High (PCIe 5.0) | High | 350W TDP | Inference, smaller training clusters |
| NVIDIA H200 SXM5 | 141 GB HBM3e | Highest (larger memory) | Highest | 700W TDP | Very large models, memory-bound workloads |
| NVIDIA A100 SXM4 | 80 GB HBM2e | High (previous gen) | High | 400W TDP | Cost-sensitive training, existing deployments |
| NVIDIA L40S | 48 GB GDDR6 | Medium | Very high | 350W TDP | Inference, computer vision, rendering |
Network Fabric Comparison
| Fabric | Bandwidth | Latency | Complexity | Cost | Recommended Scale |
|---|---|---|---|---|---|
| InfiniBand NDR (400 Gb/s) | 400 Gb/s per port | ~500 ns | High | Very high | 256+ GPUs |
| InfiniBand HDR (200 Gb/s) | 200 Gb/s per port | ~600 ns | High | High | 64–256 GPUs |
| 400 GbE + RoCEv2 | 400 Gb/s per port | 1–3 µs | Medium-high | High | 128+ GPUs |
| 100 GbE + RoCEv2 | 100 Gb/s per port | 1–5 µs | Medium | Medium | Up to 64 GPUs |
| 100 GbE (TCP/IP) | 100 Gb/s per port | 5–50 µs | Low | Low | Inference only, dev/test |
Cooling Technology Comparison
| Technology | Max Rack Density | PUE Range | Infrastructure Change | Maintenance | Cost |
|---|---|---|---|---|---|
| Precision air cooling | Up to 15 kW | 1.4–2.0 | None | Low | Low |
| Rear-door heat exchanger | Up to 30 kW | 1.2–1.5 | Chilled water to rack row | Low-medium | Medium |
| Direct-to-chip liquid | Up to 60 kW | 1.1–1.3 | Liquid manifold to each server | Medium | High |
| Single-phase immersion | Up to 100 kW | 1.03–1.1 | Complete tank infrastructure | High | Very high |
| Two-phase immersion | Up to 200+ kW | 1.02–1.05 | Complete tank + vapor recovery | Very high | Very high |
Deployment Model Comparison
| Model | Description | Lead Time | Cost | Control | Best For |
|---|---|---|---|---|---|
| New-build data center | Purpose-built facility for AI infrastructure | 18–36 months | Very high | Maximum | Large-scale, long-term AI programs |
| Existing facility upgrade | Upgrade power, cooling, network in existing DC | 6–18 months | High | High | Organizations with existing facilities |
| Colocation | Lease space in third-party data center | 3–9 months | Medium-high | Medium | Organizations without suitable facilities |
| Managed AI infrastructure | Vendor-managed AI cluster in your facility | 3–6 months | Medium | Medium | Organizations without operational expertise |
| Cloud (on-demand) | Public cloud GPU instances | Minutes–hours | Variable | Low | Variable workloads, prototyping |