RunPod GPU Inference Notes

Mental Model

GPU inference is a capacity, latency, and artifact-management problem. The code path is only one part of the system.

Design Questions

• Which model artifact is loaded?
• How large is the input and output?
• What is the cold-start cost?
• Can requests be batched?
• What is the retry behavior for failed jobs?
• Where are artifacts stored after inference?
• Which metrics explain cost and latency?

Operational Signals

• queue depth
• GPU utilization
• cold-start time
• model load time
• request latency
• job failure rate
• output artifact size

Reliability Pattern

Prefer job-based APIs for expensive inference:

1. Create a job.
2. Queue the job.
3. Run inference.
4. Store artifacts.
5. Expose status and result URLs.
6. Record failures with enough context to retry safely.