Google Colab T4 bottlenecks Llama fine-tuning

Colab’s T4 tier is great for lightweight experiments, but it becomes a bottleneck once fine-tuning workloads get serious.

• –For LoRA/QLoRA workflows, wall-clock time and preemption risk can outweigh “cheap” hourly pricing.
• –The practical alternative path is bursty rental GPUs (for example RunPod or Vast.ai) when iteration speed matters.
• –Kaggle and other free notebook options can help for experiments, but quota/runtime constraints still limit sustained tuning.
• –If staying on Colab, model size, quantization strategy, sequence length, and training stack optimizations usually decide viability.