2.4 On-Premises vs Cloud AI Infrastructure
What the exam tests
The trade-offs between running AI infrastructure on-premises vs in the cloud, and when hybrid or multi-cloud approaches are appropriate.
Decision framework
Key questions:
1. Do you have data sovereignty / compliance requirements?
2. Is your workload steady-state or bursty/unpredictable?
3. What is your CapEx vs OpEx preference?
4. Do you need custom networking (InfiniBand) for large training?
5. What is your team's operational maturity?
On-Premises
Advantages
| Factor | Detail |
|---|---|
| Data sovereignty | Data never leaves your facility; critical for healthcare (HIPAA), finance (SOX), government, and EU (GDPR) |
| Latency | Sub-millisecond to data sources; critical for real-time inference connected to on-prem systems |
| Cost at scale | At sustained, large-scale GPU utilization (>70%), CapEx typically beats OpEx over 3–5 year horizon |
| Customization | Choose exact GPU generation, network fabric (InfiniBand), cooling method; no shared-tenancy constraints |
| Network performance | InfiniBand at 400/800 Gbps within your cluster — not available in most public clouds |
| Predictable performance | No noisy-neighbor effects; dedicated hardware |
Disadvantages
| Factor | Detail |
|---|---|
| High upfront cost | DGX H100 ~$400K+ per system; full cluster = tens of millions |
| Long lead times | GPU supply constraints; 6–18 month procurement cycles at scale |
| Operational burden | Requires specialized team: network engineers, sysadmins, cooling/power ops |
| Inflexible capacity | Over-provisioning waste; under-provisioning blocks projects |
| Facility requirements | Liquid cooling, power upgrades, physical security |
Cloud
Advantages
| Factor | Detail |
|---|---|
| No CapEx | Pay-per-use; OpEx model; immediate access to latest GPUs |
| Elasticity | Scale from 0 to thousands of GPUs in minutes |
| Speed to start | Provision an 8-GPU H100 instance in minutes |
| Managed services | Cloud-managed Kubernetes, databases, MLOps pipelines |
| Geographic distribution | Multiple regions for low-latency inference globally |
| Latest hardware | Cloud providers adopt new GPU generations quickly |
Disadvantages
| Factor | Detail |
|---|---|
| Cost at scale | Sustained large GPU usage becomes very expensive; spot instances mitigate but add complexity |
| Data transfer costs | Moving large training datasets to/from cloud can be expensive and slow |
| Limited InfiniBand | Most cloud GPU clusters use Ethernet; for largest training runs, on-prem InfiniBand is faster |
| Data privacy | Regulated industries may face restrictions on cloud data processing |
| Performance variability | Shared infrastructure; noisy-neighbor effects possible |
| Lock-in | Cloud-native tools create vendor dependency |
Hybrid approach
The most common enterprise architecture:
┌──────────────────────────────┐
│ Cloud │
│ - Burst training capacity │
│ - Dev/test environments │
│ - Global inference endpoints │
└──────────────┬───────────────┘
│ VPN / Direct Connect
┌──────────────┴───────────────┐
│ On-Premises │
│ - Production training cluster │
│ - Sensitive data processing │
│ - Edge inference (DGX/Jetson) │
│ - Core model registry │
└──────────────────────────────┘
Common pattern:
- Train on-prem with your regulated/proprietary data
- Burst to cloud for extra capacity during peak demand
- Deploy inference on cloud for global reach
NVIDIA solutions for each deployment
| Environment | NVIDIA product |
|---|---|
| On-premises training | DGX systems + InfiniBand + Base Command Platform |
| On-premises inference | NVIDIA-Certified Servers + Triton + TensorRT |
| Cloud training | DGX Cloud (partnership with major cloud providers) |
| Cloud inference | NGC containers on cloud GPU instances + Triton |
| Hybrid management | NVIDIA Fleet Command (edge + cloud management) |
| Edge deployment | NVIDIA Jetson / Orin modules |
Comparison summary
| Dimension | On-Premises | Cloud |
|---|---|---|
| Cost model | CapEx (high upfront) | OpEx (pay per use) |
| Latency | Very low | Low to medium |
| Data control | Full | Shared / contractual |
| Scale flexibility | Low | Very high |
| Operational overhead | High | Low (managed services) |
| Latest GPU access | After procurement | Immediate |
| Best for | Sustained, large-scale, regulated | Bursty, experimental, global |
Self-check questions
- Name two scenarios where on-premises is the better choice over cloud.
- What is the main cost advantage of cloud for AI workloads?
- Why might a large training cluster on-premises outperform the equivalent cloud setup?
- What NVIDIA platform manages hybrid edge + cloud GPU deployments?
- What is the “noisy neighbor” problem in cloud AI compute?
Answers
1. Any two: data sovereignty/compliance requirements; sustained high GPU utilization where CapEx beats OpEx; need for custom InfiniBand fabric; latency-sensitive real-time inference connected to on-prem systems.2. No upfront CapEx — pay only for GPU time actually used; ability to scale to zero (no idle cost) and to burst to thousands of GPUs without procurement delays.
3. On-premises InfiniBand fabric (NDR 400G/800G) provides far higher bandwidth and lower latency than cloud GPU cluster networking (typically Ethernet). For training large models with high inter-node gradient communication, InfiniBand's performance advantage directly reduces training time.
4. NVIDIA Fleet Command.
5. Shared physical infrastructure means other tenants' workloads can contend for CPU, memory, network, or storage resources even when GPU instances are dedicated. This causes performance variability — your training run runs slower during peak cloud utilization periods.