Outcome#
You can correct an unsafe GPU request, justify one explicit model, plan the smallest useful smoke test, monitor bottlenecks, and define evidence required before multi-GPU scaling.
Concept#
Dataset size does not prove multi-GPU scaling. An idle or CPU/I/O-bound GPU job wastes scarce shared resources, and a long interactive tunnel can remain allocated after useful work stops.
Worked Example#
The parser accepts one explicit GPU, no forced partition, and bounded companion resources; optional live smoke testing remains a separate deliberate action.
A correct example uses these decisions:
- Which account provides the documented lab GPU access? The approved es_fuge share.
- What should a one-GPU starter request do? Request one explicit GPU and bounded CPU, memory, and time.
Common Trap#
Assuming GPUs are on the public share, requesting all node CPUs for one GPU, or submitting the review fixture accidentally.
If Blocked#
Use the RTX 4090 review baseline. Do not submit duplicate jobs for multiple GPU types or select RTX PRO 6000 merely to bypass a queue. Escalate distributed training, unusual memory, or CUDA compatibility to the supervisor.
Useful references:
Understand Before Accepting AI Output#
Verify that program operations, not only CUDA detection, use the GPU. You must explain every requested resource and why adding GPUs may not help a CPU-bound pipeline.