Unverified50% confidenceFactExact time
The publicly released VKORE-46M-NPU is a 46M-parameter, character-level model trained on the TinyStories dataset, and its purpose is to verify the feasibility of the training path rather than model quality.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/15/2026
First Seen
Valid until: 1/13/2027
Sources
Related Entities
Related Claims
UnverifiedVIDRAFT released a small language model named VKORE-46M-NPU with 46 million parameters, pretrained entirely from random weights, and the training process did not use any GPU.77% similarUnverifiedVKORE's implementation approach is to recombine, in software, the training compute primitives missing from inference-only NPUs (such as gradient computation and weight updates) based on the low-level operations the NPU itself supports, and to maximize hardware utilization through scheduling.73% similarUnverifiedThe core training technology of VKORE-46M-NPU is proprietary, is in the process of patent application, and details will be published in a subsequent paper; the core training code has not been open-sourced for the time being for intellectual property protection.70% similarUnverifiedVKORE's numerical behavior during training on NPUs is consistent with standard training; it is real training, not an approximation.70% similarUnverifiedThe pretraining and fine-tuning paradigm originated from NLP and computer vision, with its core being large-scale data pretraining to learn general low-level features, and downstream tasks adapted using a small amount of labeled data.69% similar
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