Estimation question
Estimate how many GPUs OpenAI needs to serve 1 billion weekly ChatGPT users.
- OpenAI
- Estimation
- Hard
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What this question tests
Tests structured estimation: converting a user base and usage pattern into an infrastructure requirement.
How to approach it
- Estimate daily active usage: assume 1 billion weekly users translates to roughly 300 million daily active users, with an average of 10 messages sent per active user per day.
- Calculate daily message volume: 300 million users times 10 messages is 3 billion messages a day.
- Estimate compute per message: assume each message requires roughly 1 second of GPU inference time on a modern accelerator for a typical response length.
- Calculate total daily GPU seconds needed: 3 billion messages times 1 second is 3 billion GPU seconds a day, or roughly 35,000 GPU hours running continuously every hour of the day when annualized per hour.
- Convert to GPU count: 3 billion seconds divided by 86,400 seconds in a day is roughly 35,000 GPUs running continuously to handle that steady state load, then add meaningful headroom, roughly 2 to 3 times that, for peak traffic and redundancy, landing around 70,000 to 100,000 GPUs.
What a strong answer includes
- Builds the estimate step by step from users to messages to compute time to GPU count, rather than jumping to a final number.
- Explicitly accounts for peak versus average load with a headroom multiplier, since steady state average demand understates the GPUs actually needed to handle real world traffic spikes.
- Converts total compute seconds into an intuitive GPU count using a clear unit conversion, showing the math transparently.
Common mistakes
- Estimating only average load and ignoring the need for headroom to handle peak traffic and redundancy.
- Skipping the message volume calculation and guessing a GPU count directly.
Likely follow-up questions
- How would this estimate change if a large share of usage came from longer, more complex conversations?
- What would happen to the GPU requirement if model efficiency improved by half?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 2: Data fluency: SQL, logs, and reading the truth yourself
- Chapter 9: Prove it paid off: outcomes, economics, and pricing
- Chapter 14: Get the job: the AI PM interview loop