Deep Dive: Predicting Kimi K3 Model Architecture and Training
_xjdr · x · 2026-07-17
A blogger makes hardcore predictions about Kimi K3's potential architecture and training scheme based on current LLM trends:
- Base architecture: Recommends the DeepSeek V3 (DSV3) architecture style, introducing at least a 4:1 sliding window attention mechanism.
- Parameters and training: Speculates total parameters will reach 1T, trained using Rollout data generated by the K2 model.
- Optimization and compute: Uses muon and mup optimizers, trained on the latest GB300 systems.
- Core evaluation: Believes this sparse Mixture-of-Experts (nmoe) design is the exact route Meta should have taken for Llama 4 and subsequent versions.
Related event: Kimi K3 Debuts Strong, Narrowing the Open-Weight Gap(184 posts)→
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