Compute College

About Compute College

Compute College is a free practical learning platform for AI workloads and AI compute.

ComputeTape Market Intelligence provides the pricing, provider data, reports, power signals, and market evidence used throughout the courses.

Practical learningPurpose

Short lessons and calculators that explain how AI workloads use hardware, software, power, and cloud capacity.

Source-backedEvidence

Market claims carry sources, timestamps, and caveats so learners can see how an explanation connects to evidence.

Free and practicalAccess

Start with the fundamentals, follow a track, or jump directly to a concept, calculator, or market question.

Learn how training, inference, tokens, context, utilization, networking, memory, and power shape usable AI compute.
Compare GPUs, cloud capacity, model workloads, and deployment choices in plain English.
Use calculators to turn GPU-hours, token usage, runtime, and utilization into illustrative workload estimates.
Understand pricing, availability, infrastructure, and market structure only after learning what those signals measure.
Keep current claims connected to source labels, observation timestamps, methodology, and caveats.

Provides reviewed GPU pricing, provider data, capacity context, monthly reports, Power Watch signals, Compute Futures coverage, and the Market Brief.
Separates measured or sourced market evidence from interpretation and from illustrative lesson arithmetic.
Helps learners connect a concept to the real decisions made by builders, operators, buyers, investors, and infrastructure teams.

Builders and product teams who want to understand the cost and behavior of the workloads they are creating.
Operators and procurement teams comparing capacity, reliability, pricing, and deployment tradeoffs.
Students, analysts, investors, and curious readers who want a grounded explanation of AI infrastructure without hype.

Learn the basic units: GPUs, GPU-hours, training, inference, and usable compute.

Understand token spend, serving, utilization, and the cost of completing real workloads.

See how electricity, cooling, memory, networking, and site readiness constrain capacity.

Apply the concepts to reviewed prices, provider data, reports, power signals, and futures.

Understand the systems behind AI workloads