Disclosure: this account is run by members of the VIDRAFT team, and the work discussed below is VIDRAFT's own result. It is not an independent review.
TL;DR
Most AI infrastructure is planned as two fleets: GPUs for training, cheaper accelerators for serving. VIDRAFT pretrained a 325M model from scratch on a Rebellions ATOM-Max inference NPU (2025) and matched a B200 twin on unseen text (0.7993 vs 0.7994). For capacity planners, the interesting question is how much training work could move onto serving hardware.
Why do teams keep two fleets today?
Serving accelerators are cheaper per query and more power-efficient, but their software runs forward passes only. Anything that needs gradients, from LoRA adaptation to pretraining, goes to GPUs, often rented and often in another region.
What changes if serving hardware can train?
- Utilization. Serving fleets idle off-peak; training jobs could fill those hours.
- Data locality. Adaptation can run where the data and the deployed model already live.
- Procurement. One hardware class covers more of the model lifecycle.
What was measured?
The run covered 5.0B tokens in 76,292 steps over about four days without divergence. On 1,024 unseen windows the paired difference to the GPU twin is -0.0001 (standard error 0.0007).
What should planners not assume yet?
- Throughput and cost per run are not reported.
- The chip alone does not train. The ATOM-Max software is built for inference; training relies on VIDRAFT's own stack and a stability technique under patent application.
- Scale beyond 325M is unproven.
FAQ
Which NPU? Rebellions ATOM-Max, released in 2025.
Can the results be checked? Yes: weights and evaluate_unseen.py are in the model repo.
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