AI Hardware Estimator
Estimate practical hardware requirements for popular AI models with conservative recommendations for VRAM, GPU count, system RAM, and storage. This AI Hardware Estimator is built for teams planning inference or fine-tuning capacity before purchasing infrastructure.
Estimates are directional planning guidance, not a performance guarantee. Validate against model architecture, context length, concurrency, and production reliability targets.
Estimator Inputs
Set your assumptions, then generate conservative sizing guidance for early planning and procurement discussions.
Estimated Requirements
Directional System Class
Single-Node Inference Build
Prioritizes reliable single-node inference capacity for initial deployment.
- Estimated VRAM required
- 16 GB
- Recommended GPU tier
- Entry Pro (16 GB class)
- Recommended GPU count
- 1 GPU
- Recommended system RAM
- 64 GB
- Recommended storage tier
- 1 TB NVMe
- Suggested build class
- Single-Node Inference Build
Why this result
- Llama 3 8B at FP16 sets the baseline memory requirement.
- Standard workload and inference assumptions increase buffer targets for VRAM and system RAM.
- 1 GPU and 1 TB NVMe indicate a single-node inference build profile rather than minimum-fit hardware.
What moves you up or down a tier
- Move up a tier if you expect larger models, higher concurrent request load, or want additional VRAM headroom for growth and reliability.
- Move down a tier only when workload shape is stable (smaller models or lower precision) and utilization targets are intentionally constrained.
This estimate is directional sizing. Use Builder to verify exact component compatibility, power envelope, and procurement-grade configuration details.