$NBIS GPU Rental Prices are up 20% Let’s compare what these new On-Demand rates imply on a theoretical fully utilized $/MW basis to Dedicated Contracts (hyperscaler, enterprise, short term training). 🧧 What drives Pricing Premium? Primarily by contract duration. The shorter the commitment, the higher the price per unit of compute, as $NBIS takes on more utilization and recontracting risk. Of course prepayments, credit backstops, and contract size also shape the economics. That puts On-Demand at the top of the pricing stack. Instead of locking GPUs into dedicated contracts, $NBIS retains that risk and sells the same compute in smaller increments at materially higher unit economics. ◽️What GPU/Hour Pricing Actually Includes These are standard AI Cloud GPU-instance rates, quoted per GPU/HR. Each instance comes with the GPU plus its allocated vCPU and RAM. $NBIS also includes Managed Kubernetes, Soperator software, ingress/egress networking, and public IPs at no extra platform charge. Storage is separate, including block, object, and shared filesystem storage. 🏭 These are NOT Token Factory prices @nebiustf is priced around managed inference consumption, which layers on incremental premium on top of base AI Cloud rates for model serving, model-specific tuning, and performance optimization 🎣 Caveats: 1. On-Demand rate assumes 100% GPU utilization 2. Dedicated contract rates are not directly comparable. As mentioned there are other factors beyond duration that influence pricing. 3. We also don't know silicon blend for these hyperscaler, enterprise, and short-term scale training contracts 4. B300 ≠ GB300 from a power-density standpoint. HGX B300 is ~1.1kW/GPU, while GB300 NVL72 runs materially higher. It’s unclear which configuration Nebius’ posted B300 rate refers to. 5. Posted On-Demand rates are list prices, not necessarily realized ASPs 6. On-Demand $/MW reflects GPU power only, not total facility power. CPU, networking, cooling, and PUE may not be subsumed, which would lower realized revenue per facility MW.