Unverified50% confidenceBenchmarkExact time
The milestones publicly demonstrated by VKORE include: training a 46-million-parameter model from scratch, running on 32 inference-only NPU chips, with a loss correlation of 0.99 with a GPU training baseline.
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50%
Confidence
Short-term (~14 days)
Relevance
10/16/2026
First Seen
Valid until: 10/30/2026
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UnverifiedVKORE-46M-NPU has a parameter scale of 46M, about one-third of GPT-2 Small (about 124 million parameters), and its significance lies in validating that the NPU training path is feasible, rather than in the quality of the model itself.74% similarUnverifiedThe source does not provide quantitative benchmark comparisons for VKORE-46M-NPU, including perplexity scores, downstream task accuracy, and training throughput in tokens per second.73% similarUnverifiedVKORE and PyTorch achieve a numerical consistency of 0.9999 in LoRA training.70% similarUnverifiedNo Decision 3.0 model was exposed to video data during training, yet on all 19,140 questions in the Perception Test validation set, the 27B version achieved a zero-shot score of 77.4 and the 4B version scored 70.3.69% similarUnverified2012年Alex Krizhevsky等人使用AlexNet在ImageNet竞赛中将top-5错误率从26%降低到15.3%，该突破基于两块GTX 580显卡的CUDA加速训练实现66% similar
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