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How to Prioritize AI Projects Amidst GPU Constraintsby@prerakgarg
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How to Prioritize AI Projects Amidst GPU Constraints

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The rise of Generative AI, driven by large language models like GPT-4, is reshaping tech industry strategies. Incorporating AI features via LLMs has become crucial, but a challenge arises due to the ongoing GPU shortage and high costs. The demand for high-end GPUs (e.g., A100s, H100s) for AI services has overwhelmed manufacturers, causing a supply shortage. Even major cloud platforms like AWS and Azure have had to implement quota systems. GPU shortage is affecting OpenAI's ChatGPT advancement, hindering API availability and larger "context windows." Tech product leaders face a dilemma: delivering AI-powered features while dealing with GPU constraints. They must strategically prioritize products using a new framework based on Contribution Per GPU. The proposed framework involves identifying metrics like Revenue, Market Share, and Daily Active Users, calculating Contribution Per GPU, and prioritizing products accordingly. While this approach provides strategic clarity and objectivity, it may not capture all strategic aspects. Exceptional cases should be considered thoughtfully. The GPU shortage challenge can be turned into an opportunity by using the Contribution Per GPU framework, helping companies maximize ROI and focus on long-term success.
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