Chip Sense / Tools / AI cluster demand model
AI cluster demand model
Bill-of-materials chain for teaching: clusters → GPUs → HBM → packaging slots → implied logic wafers. Directional demand under your assumptions — not a hyperscaler forecast.
How to use
- Review Assumptions on the left. Amber badges are teaching estimates; they become Your input when you edit.
- Set how many clusters and GPUs per cluster for your what-if deployment.
- Adjust HBM stacks/GB and yields to match the SKU you have in mind.
- Read implied GPUs, HBM, packages, and logic wafers — directional only.
- Read Estimated results on the right (~ means estimate). Use Reset or Methodology anytime.
Assumptions
Edit any field — values you change are treated as your input, not Chip Sense published facts.
Estimated results
Outputs use ~ / ranges on purpose. Basis: your current assumptions.
GPUs required
Estimate~8GPUs
Clusters × GPUs per cluster.
HBM stacks
Estimate~48stacks
GPUs × stacks per GPU.
HBM capacity
Estimate~0.750TB
Stacks × GB/stack ÷ 1024.
Packages needed (w/ pkg yield)
Estimate~8.7packages
GPUs ÷ package yield (scrap/rework teaching factor).
Implied logic wafers
Estimate~0.229wafers
GPUs ÷ (DPW × die yield). Ignores multi-die packages and HBM wafer demand.
Pair with Yield and Packaging cost for essay screenshots.