AI Inference Is Obviously Profitable: Breaking Down the Economics

Sean Goedecke argues that AI inference is obviously profitable, contrary to claims that it's subsidized by VC money. He breaks down the math and open-model pricing to show that serving LLMs can be a sustainable business.

Cost Breakdown for a 70B Dense Model

Using four Nvidia A100 GPUs (400W each, ~2M tokens/hour):

• Power: ~13¢/hr at industrial rates, plus ~13¢/hr cooling → 26¢/hr total
• GPU amortization: $20k per A100 over 5 years → $16k/yr or $1.80/hr
• Total: roughly $1 per million output tokens

OpenAI's GPT-5.4-mini charges $4.50 per million tokens, and stronger models are 3-6x more expensive. This makes the claimed 70-80% gross margin plausible.

Open Models Confirm Profitability

DeepSeek claims over 80% margin on R1 inference while charging less than half of OpenAI/Anthropic. Their DeepSeek-V4-Pro API is around 87¢ per million output tokens—close to actual cost, suggesting margins for frontier models are even higher.

Why High Margins Exist

AI labs like OpenAI and Anthropic need inference profits to subsidize training costs, so they keep API prices high. But an inference-only provider without training costs could profit even at lower prices. Even if frontier labs go under, whoever acquires their model rights can continue selling inference profitably.