AI on your own infrastructure
The board wants an AI strategy; your data can't leave the building; the cloud GPU bill quotes are startling. Running AI on your own infrastructure is the answer for a specific set of situations: sensitive data, steady inference load, predictable costs, or latency that matters. It is not the answer for occasional experimentation, which the cloud serves better.
The sizing question is more tractable than it looks. Inference is the workload most estates actually need, and it is bounded by model footprint and concurrent load, not by the training-cluster numbers in the headlines. A capable inference host is a rack server with the right GPUs, a lot of memory, and fast local storage: familiar hardware, sized honestly.
What actually determines the answer
| Factor | Why it matters |
|---|---|
| Where the data can live | If the data can't leave your environment, the model comes to the data. This single constraint decides most private AI cases. |
| Steady load versus experimentation | Constant inference load pays for owned hardware quickly. Bursty experimentation is what cloud GPUs are for. |
| Model footprint | The models you'll actually serve determine GPU memory, and GPU memory determines everything else about the host. |
| Who operates it | An inference platform is a production system: patching, monitoring, model updates. Decide who owns that before the hardware arrives. |
The paths, with their trade-offs
Dedicated inference hosts
When you have steady inference or RAG load and data that stays home.
Trade-off: Capacity is fixed: size for peak concurrency honestly or queue at the busy hour.
Configured BOM
- CPU 2× Intel Xeon 6 processor, 32 cores each
- GPU 2× datacenter inference GPUs, sized to your model footprint
- Memory 16× 64 GB DDR5 RDIMM (1 TB total)
- Storage 4× 3.84 TB NVMe SSD for model and vector storage
- Network Dual-port 100 GbE adapter
- Support 4-year ProSupport Plus, next business day
Extend the virtualization estate with GPU capacity
When AI is one workload among many and utilization will be mixed.
Trade-off: Shared hosts mean shared contention; latency-sensitive inference may deserve its own box.
Configured BOM
R770 - 60 VM virtualization host
- CPU 2× Intel Xeon 6 processor, 48 cores each
- Memory 8× 64 GB DDR5 RDIMM (512 GB total)
- Storage 6× 3.84 TB NVMe read-intensive SSD
- Network Dual-port 100 GbE adapter
- Support 4-year ProSupport Plus, next business day
Dedicated training hardware
When you are fine-tuning or training models on your own data at real scale.
Trade-off: The most expensive path by far, and it needs a facility review before it needs a quote. Rent first, buy when the load proves steady.
Configured BOM
- Accelerators 8× SXM-class GPUs with high-bandwidth interconnect
- CPU 2× Intel Xeon Scalable processors
- Memory System memory sized to the GPU configuration
- Storage NVMe local scratch for datasets and checkpoints
- Fabric High-bandwidth cluster networking, sized per node count
- Support 4-year ProSupport Plus, mission critical
Stay in the cloud for now
When you're still finding the use case and load is unpredictable.
Trade-off: The honest answer more often than vendors admit. Revisit when the load curve flattens.
Run your own numbers first: VM sizing calculator →
What deployment involves
Typical scope: host build and GPU configuration, model serving stack, connection to your data sources, and a monitored handover.
We also operate what we deploy: agent-handled routine operations with a named human accountable, if you want the platform run rather than just installed.