[simonw]

If you have 1,000 researchers working for your company and you constantly have dozens of different training runs in the go, overlapping each other, how would you split those salaries between those different runs?

Calculating the cost in terms of GPU-hours is a whole lot easier from an accounting perspective.

The papers I've seen that talk about training cost all do it in terms of GPU hours. The gpt-oss model card said 2.1 million H100-hours for gpt-oss:120b. The Llama 2 paper said 3.31M GPU-hours on A100-80G. They rarely give actual dollar costs and I've never seen any of them include staffing hours.

[shawndrost]

Do they include the costs of dead-end runs?

[Der_Einzige]

No, they don't! That's why the "5.5 million" deepseek V3 number as read by American investors was total bullshit (because investors ignored their astrik saying "only final training run")

[simonw]

Yeah, that's one of the most frustrating things about these published numbers. Nobody ever wants to share how much money they spent on runs that didn't produce a useful model.

As with staffing costs though it's hard to account for these against individual models. If Anthropic run a bunch of training experiments that help them discover a new training optimization, then use that optimization as part of the runs for the next Opus and Sonnet and Haiku (and every subsequent model for the lifetime of the company) how should the cost of that experimental run be divvied up?