AI Workload Design Scorecard
Turn a vague AI idea into a workload and architecture checklist before choosing a model or provider.
Compute College Tools
Free calculators for estimating GPU-hour cost, training-run budgets, and model serving cost from your own inputs. Every calculator shows its assumptions and links to the lesson that explains the math.
Estimates are planning tools, not quotes.
AI Engineering labs
Start with browser-local learning tools for workload design, context budgets, prompt evaluation, retrieval quality, agent permissions, and accepted outcomes. No account, model API, or market quote is required.
Turn a vague AI idea into a workload and architecture checklist before choosing a model or provider.
See how instructions, retrieved material, and reserved output consume a context window.
Compare accepted results, retries, and review time across prompt versions.
Explore precision, recall, and the token cost of adding retrieved chunks.
Estimate token cost and cost per successful request from traffic and success rate.
Classify tool actions by risk, reversibility, approval, and automation.
Include failed attempts and review in the cost of producing an accepted result.
Estimate cost
Three calculators for sizing a budget from scratch. Each runs in your browser and updates as you type.
Estimate AI compute cost from GPU price, runtime, utilization, and overhead.
Estimate a training-run budget using GPU-hours and operating assumptions.
Estimate recurring inference cost from usage and capacity needs.
Decide between options
Two side-by-side decision tools for the most common AI compute trade-offs. Each one produces a break-even and a recommendation.
Compare API serving cost to a self-hosted GPU cluster, with break-even output volume and utilization sensitivity.
Compare a GPU reservation to pure on-demand; see break-even GPU-hours and stranded capacity.
Convert infrastructure
When a number lands in megawatts, racks, or square feet, this is where you turn it into a GPU range.
Translate an announced megawatt number into a GPU-capacity range using PUE, rack density, and utilization.
Decision guides
These lessons walk through the trade-offs behind each number — access terms, utilization, and how to read a quote.
Compare access terms, savings, and interruption risk.
Compare rate, reliability, network, and completed-workload cost.
Turn training and serving usage into a recurring budget.
Why paid capacity can cost more when it sits idle.
Compare quotes
GPU cloud quotes are not just rates. The checklist below names the eleven fields to record on every offer so two quotes can be read side by side without confusion. Use it as the column header for your own comparison sheet.
Eleven fields to record on every quote: GPU generation, effective cost, commitment, region, capacity, networking, storage and egress, SLA, interruption, flexibility, and quote source date.
The narrative method behind the checklist.
Start with the basics
Compute College explains GPU-hours, utilization, training cost, and serving cost in plain English before you reach for a calculator.