Samsung Securities: Hyperscaler Compute Business Models

Hyperscalers operate through two primary methods: 1) Computing rental and 2) Model inference services, both of which can achieve a Gross Profit Margin (GPM) of 68% to 81%.

> Model Inference Advantages: Model inference services feature a large revenue base and high margins, which is why Meta focuses on deploying its capacity toward in-house services rather than plain rentals.

> Value of Compute Scarcity: As seen in examples like SpaceX, simple rental services based on compute scarcity can achieve revenue and margin levels comparable to model inference services.

> Widening Gap: The flexibility and profitability gap between operators capable of deploying and servicing massive compute infrastructure versus those that cannot is expected to widen further.

Chart Breakdown

> Compute Rental Service GPM (Left Chart): Shows $21.7B in revenue, $6.9B in cost of goods sold (COGS), resulting in $14.8B in gross profit, yielding a 68% GPM.

> Open-Weight Model Inference Service GPM (Middle Chart): Shows $36.6B in revenue, $6.9B in COGS, resulting in $29.7B in gross profit, yielding an 81% GPM.

> Hyperscaler GPM Comparison by Compute Business (Right Chart): Compares compute rental (GPM 68%, $14.8B gross profit), model inference with 35% training (GPM 71%, $16.9B gross profit), and model inference with 0% training (GPM 81%, $29.7B gross profit).

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