Hey there, I’ve dealt with this frustrating GPU limit issue in Colab more times than I can count—here are the practical fixes that’ve gotten me back up and running:

• Wait for quota reset
  Colab’s free GPU quota refreshes on a rolling 24-hour basis. If you’ve hit the limit, step away for a few hours (usually 6-24 hours depending on your recent usage) and try again. Also, head to Runtime > Manage sessions to close any idle or unused sessions—they might be eating into your quota without you realizing it.

• Cycle your runtime type
  Sometimes switching to a different runtime and back can trigger a new GPU allocation. Go to Runtime > Change runtime type, select "TPU" (or even "None" temporarily), save the setting, then switch back to "GPU" and restart the runtime. This small trick often works when the quota system is being finicky.

• Clean up GPU resources in your code
  Leaking GPU memory can make you hit limits faster. Add cleanup commands to your workflow:

  • For PyTorch: torch.cuda.empty_cache() after training loops or model evaluations
  • For TensorFlow: tf.keras.backend.clear_session() to free up unused graph resources
    Make sure you’re not keeping unnecessary tensors or models loaded in memory when they’re not needed.

• Upgrade to Colab Pro/Pro+
  If you rely on Colab for regular GPU work, the paid tiers are worth considering. Pro gives you higher GPU quotas, priority access to GPUs, and longer session times. Pro+ takes it a step further with access to premium GPUs like A100 and even fewer restrictions.

• Avoid triggering abuse detection
  Colab limits users who seem to be misusing resources—like leaving sessions running idle for hours, spawning multiple sessions at once, or running scripts that don’t do meaningful work. Stick to running only necessary code, close sessions when you’re done, and avoid automating repeated session launches.

内容的提问来源于stack exchange，提问作者gandayz