Free infrastructure planning tool

Dedicated AI Server Advisor: GPU Sizing and Cost Comparison

Pick a model and expected workload, explore a GPU memory envelope, and compare your own dedicated-server and cloud quotes. The result is a purchase-planning checklist, not an automatic hardware-fit certification or a measured throughput benchmark.

Step 1 · Model and capacity

Build a server sizing envelope

Calculated · not a fit test

Step 2 · Planning output

Estimated server capacity

Planning VRAM

8.7 GB

4.7 GB artifact + 4 GB user context reserve

GPU envelope

1 × 24 GB

19.2 GB usable/card after reserved headroom

Host RAM reference

48 GB

NVIDIA inference design reference: 2× aggregate VRAM; validate your system

CPU reference

6 physical cores

NVIDIA reference: 6 cores/card; not a minimum for all deployments

Storage and deployment checklist: At least 10 GB to retain two copies of this model artifact alone; provision additional OS, NVMe working space, caches, datasets, logs and backups. Check GPU cooling/power, PCIe lanes, ECC/support requirements, expected batch/context limits, security and recovery procedures.

Model source checked 2026-10-01: Ollama artifact evidence. Its maximum published context capability (32K) is not the memory reserve selected above.

Step 3 · Enter comparable provider quotes

Dedicated server vs hourly GPU rental

Prices default to zero because no provider quote is assumed. Both the dedicated monthly price and comparable cloud hourly price are required to calculate a result.

Enter both provider prices above to see a cost comparison. No default market prices or artificial savings are presented.

What to verify before buying

  1. Confirm the exact model artifact, runtime, quantization and requested context.
  2. Measure actual peak GPU VRAM and KV/cache usage at production concurrency.
  3. Verify single-card fit or an explicitly supported multi-GPU architecture.
  4. Validate CPU, system RAM, storage IO, GPU power and chassis cooling.
  5. Compare true billing scope, reliability, egress, maintenance and operator time.
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What the calculation means

Planning VRAM = published model-file footprint + your context allowance per concurrent request. Usable VRAM per GPU = stated capacity minus your reserve percentage. The advisor selects the smallest 1, 2, 4 or 8 GPU envelope that contains the estimate. This does not establish that the runtime can distribute model weights and cache across cards, or that a configuration meets latency targets.

For professional NVIDIA inference-server configurations, the manufacturer recommends validating sufficient host CPU cores, RAM capacity, GPU topology and PCIe lanes. It cites approximately six physical cores per GPU and host RAM of at least twice total GPU VRAM as reference design guidelines; those are not universal small-server minima.

Sources and validation