Distributed Training
One GPU stops being enough for three distinct reasons — training is too slow, the model doesn't fit in memory, or the batch size that would actually converge well doesn't fit either — and each has a genuinely different fix. Reaching for the wrong one wastes both engineering effort and compute budget.