Not All GPU's Are Deployed The Same#

Oliver Kennon (SemiAnalysis, Research Analyst, Core Research)

• Why: The most substantiated talk in the program for anyone buying or operating GPU capacity. It replaces "GPU-hour price" — the number most procurement conversations still use — with an operator-level economics framework, and it does so with measured numbers rather than slogans.
• What: A commodity-market analysis of inference compute. GPU hours are an input, not output; the useful metric is full cost per successfully completed task at required quality. The core demonstration, from SemiAnalysis's public InferenceX calculator: two B200 deployments serving the same model (DeepSeek-R1-0528, FP4, ~8K in / 1K out tokens, 100 tok/s per-user P50 target) produce 199 vs 298 generated output tokens per second per GPU — ~50% more output from the same chip-hour — purely from serving-stack, settings, and concurrency choices. Kennon also cites SemiAnalysis's own published security work finding cross-tenant exposure via monitoring access as an example of a failure no throughput benchmark catches.
• How: Cost-curve framing (you can improve 10→8 while a competitor goes 12→6 and you become the high-cost producer), ClusterMAX managed-cluster ratings (provisioning, reliability, monitoring, security, pricing, availability — explicitly scoped to managed clusters, not bare metal), and a careful argument about what a rental-price index and compute futures can and cannot hedge: a rating is a building block for a credible futures market, not a grade or a hedge by itself.
• Where applicable: Broadly applicable framework for anyone procuring or operating inference capacity. The B200 throughput numbers are workload-specific (that model, that token ratio, that latency target), and ClusterMAX tiers are relative to peers. Relevant to GPU cluster planning, benchmarking practice, and capacity economics.