Reddit user u/Significant-Serve-58 trained a 1.17B parameter sparse Mixture-of-Experts model with 348M active parameters on ~11B tokens using a single RTX 5090. The model achieves 85.5% accuracy on shuffled fact chain evaluations, significantly outperforming Qwen2.5-1.5B's 53.3% despite Qwen's 18T token training corpus. The author released GGUF weights and an evaluation set for community validation, emphasizing the model is specialized for factual reasoning rather than general assistance.