a16z Podcast
The True Cost of Compute
Aug 7, 2023
In this discussion, Guido Appenzeller, an A16Z Special Advisor and infrastructure aficionado with a rich background at Intel, dives deep into the economic realities of AI hardware. He reveals the staggering costs of training models and questions the sustainability of current compute investments. The conversation also highlights the distinct financial dynamics between training and inference. As AI matures, Appenzeller emphasizes that efficient hardware is crucial, setting the stage for future innovations in technology.
14:27
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Quick takeaways
- Training large language models can cost millions to tens of millions of dollars, making access to compute resources crucial for the success of AI companies.
- Inference is cheaper and faster compared to training, but provisioning for peak capacity usage in inference can significantly impact costs.
Deep dives
The high cost of training AI models
Training large language models, such as transformer models, can be incredibly expensive, ranging from millions to tens of millions of dollars. Companies are spending a significant portion of their capital on compute resources, with access to compute becoming a determining factor for the success of AI companies. Despite the cost, training these models is essential as they leverage a large proportion of human knowledge in a particular domain. As the chips get faster, there might be a slight decrease in the cost of training, but the availability of new training material remains a challenge.
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