"Leading frontier model developers are going to face trade-offs in how they use their compute resources. It's a super consequential decision these companies need to make."
"The global shortage of AI chips, likely to be exacerbated by the Middle East war's impact on helium, a key component in GPU production, along with a backlog in building data centers, means there is only a finite amount of hardware to both train and run AI models."
"Dial down the training budget and you risk falling behind competitors in releasing cutting-edge models. Cut back on inference, the speed and scale at which you meet customer demand, and you frustrate users."
AI companies are moving away from unlimited access to tokens, which were previously bundled into subscriptions or offered at low prices. Rising costs from serving models, chip shortages, and data center bottlenecks are forcing companies to ration access. Meta recently took down its productivity leaderboard, revealing employees used over 60 trillion tokens in a month. Companies face critical trade-offs in resource allocation, balancing training budgets and customer demand amidst a global shortage of AI chips and infrastructure challenges.
Read at Fast Company
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