How do I decide whether spot GPUs are safe for training?

Spot GPUs can be a great cost saver for training, but they’re “safe” only if your training setup can tolerate interruption. A good way to decide is to evaluate workload tolerance, recovery strategy, and economics.

Use spot GPUs when:

• Your training can resume from checkpoints without losing much progress.
• Interruptions are acceptable and you can requeue jobs automatically.
• You have distributed or elastic training that can handle node loss.
• The cost savings are large enough to justify some operational complexity.
• You can afford delayed completion if capacity disappears.

Avoid spot GPUs when:

• You’re doing one-off training runs that can’t easily be resumed.
• A lost run would be very expensive in researcher time or deadline risk.
• Your training is stateful and brittle (e.g., large in-memory state, hard-to-restart pipelines).
• You need predictable completion time more than low cost.
• You’re in a phase where debugging interruptions would waste a lot of engineering time.

Questions to ask before using spot:

1. Can I checkpoint frequently enough?
   Ideally every few minutes or at least often enough that rerunning is cheap.

2. How long does restart take?
   If booting, loading data, and restoring state take 20–30 minutes, frequent interruptions may erase savings.

3. What is my preemption rate?
   Estimate how often spot instances are reclaimed in your region/provider and for your GPU type.

4. How expensive is failure?
   Consider lost compute + engineer time + schedule impact.

5. Can I automate recovery?
   Good spot setups have:

   • automatic checkpointing
   • job retry logic
   • instance termination notices handled gracefully
   • artifact/version tracking

A simple rule of thumb

Spot is usually worth it if:

Expected savings > expected interruption cost

A rough way to think about interruption cost:

• cost of lost work
• restart overhead
• extra engineering time
• deadline risk

If interruptions would cause you to lose only a small fraction of work and restart automatically, spot is often a win.

Practical safeguards

If you do use spot GPUs:

• Save checkpoints to durable storage frequently.
• Make checkpoints small and fast to write.
• Test restoring from a checkpoint before large runs.
• Use job orchestration that retries on failure.
• Track experiment state externally, not only on the instance.
• Prefer training code that can recover from mid-epoch interruptions.

Best use cases

Spot GPUs are especially good for:

• hyperparameter sweeps
• large-scale experimentation
• pretraining where runs are resumable
• batch jobs with many independent tasks
• training pipelines with robust checkpointing

Worst use cases

• interactive debugging
• small but critical production training
• jobs with long warmup and no checkpoints
• experiments that must finish by a fixed time with little slack

If you want, I can help you make a spot-vs-on-demand decision checklist or estimate the break-even point for your specific training job.