ComputeAtlas

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 use the shared ComputeAtlas workload reference and distinguish model-weight floors from runtime planning headroom. Fine-tuning estimates represent parameter-efficient LoRA/QLoRA-style adaptation, not full-parameter training. See the VRAM methodology and source reference.

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
18 GB
Recommended GPU tier
Recommended GPU count
Recommended system RAM
Recommended storage tier
Suggested build class
Single-Node Inference Build

Inference planning includes a runtime reserve above the raw model-weight floor; actual KV cache, batch, framework, and context costs vary by runtime.

Why this result

  • Llama 3 8B at FP16 has an approximate 16 GB model-weight floor and a 18 GB standard inference planning target before workload scaling.
  • Standard workload and inference assumptions produce a 18 GB planning target with runtime headroom rather than a minimum-fit claim.
  • 1 GPU and 1 TB NVMe indicate a single-node inference build profile.

What moves you up or down a tier

  • Move up a tier if you expect larger models, higher concurrent request load, longer context, or want additional VRAM headroom for reliability.
  • Move down a tier only when workload shape is stable (smaller models or lower precision) and utilization targets are intentionally constrained.
Open in Builder to Validate Component Choices

This estimate is directional sizing. Use Builder to verify exact component compatibility, power envelope, and procurement-grade configuration details.