In AI agent training, you can pay for powerful GPUs and still spend much of your time waiting. Agents need to attempt tasks and produce results before the model can learn from them. A few slow attempts can hold up the next training update, making the systems that run those tasks a major factor in training speed and cost. Di Jin , VP of AI Training at Amsterdam-headquartered Nebius , says teams coming from pretraining or supervised fine-tuning can underestimate the work these systems do. “If you staff and design it as a training project, inference becomes the bottleneck nobody owns.” Rollout...

Source: The New Stack