Comment on The Extreme Cost of Training AI Models.
breadsmasher@lemmy.world 4 months ago
The source didn’t have this detail - google training gemini “cloud” vs “own hardware”. Does Google Cloud not count as “own hardware” for google?
This is an accounting trick as well, a way to shed profit, and maximize deductions, by having different units within a parent company purchase services from each other.
I realize that my two sentence explainer doesn’t shed any light on how it gets done, but ironically, you can ask an LLM for an explainer and I bet it’d give a mostly accurate response.
From the source:
Our primary approach calculates training costs based on hardware depreciation and energy consumption over the duration of model training. Hardware costs include AI accelerator chips (GPUs or TPUs), servers, and interconnection hardware. We use either disclosures from the developer or credible third-party reporting to identify or estimate the hardware type and quantity and training run duration for a given model. We also estimate the energy consumption of the hardware during the final training run of each model.
As an alternative approach, we also calculate the cost to train these models in the cloud using rented hardware. This method is very simple to calculate because cloud providers charge a flat rate per chip-hour, and energy and interconnection costs are factored into the prices. However, it overestimates the cost of many frontier models, which are often trained on hardware owned by the developer rather than on rented cloud hardware.
bjorney@lemmy.ca 4 months ago
That’s why the bars are so different. The “cloud” price is MSRP