Design GPU Scheduling for a Video Generation Platform
Company: OpenAI
Role: Software Engineer
Category: System Design
Difficulty: medium
Interview Round: Onsite
Design GPU scheduling for a video generation platform where requests have different durations, priorities, and capacity needs.
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<summary>Hint 1</summary>
Start by stating assumptions, then work from requirements to trade-offs and validation.
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<summary>Hint 2</summary>
Use concrete examples from the prompt and make edge cases explicit.
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### Constraints & Assumptions
- Preserve the source scope; do not assume extra company-specific systems.
- Focus on interview reasoning, correctness, and operational trade-offs.
- Explain how you would validate the answer with examples, metrics, or tests.
### Clarifying Questions to Ask
- What exact user, system, or business goal should this solve?
- What scale, latency, reliability, or privacy constraint matters most?
- What existing infrastructure or code must the solution integrate with?
- What output or behavior will the interviewer use to judge success?
### What a Strong Answer Covers
```premium-lock What a Strong Answer Covers
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### Follow-up Questions
- How would your answer change at 10x scale?
- What would you monitor in production?
- What edge case is easiest to miss?
- What would you simplify if this were a 60-minute implementation round?
Quick Answer: Prepare for a OpenAI system design interview about design gpu scheduling for a video generation platform. This question focuses on requirements, architecture, trade-offs, reliability, and follow-up discussion areas.