Backpropagation on an Inference-Only NPU: Pretraining VKORE-46M-NPU From Scratch

FINAL-Bench/VKORE-46M-NPU is a 46-million-parameter decoder-only transformer pretrained from scratch on 32 first-generation inference-oriented NPUs, challenging the assumption that such hardware can only perform inference. Using a character-level tokenizer and the TinyStories dataset, the project implemented backpropagation and optimization despite obstacles including static compiled graphs, immutable-weight assumptions, limited training precision, optimizer-state memory demands, and absent collective communication tools. Its main evidence is a controlled run with matched initialization, data ordering, and schedule against a GPU reference, where loss trajectories reportedly achieved a 0.99 correlation and comparable throughput. The released model, weights, model card, and comparison data support inspection and inference use, but the patent-pending training method and code remain private, limiting independent replication. While the small model and simplified dataset do not demonstrate large-scale training quality, the result suggests that future inference accelerators may potentially support both training and serving workloads, with implications for energy use, on-device adaptation, and data-local model development.

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