Nemotron-Cascade-2 hits 10GB Mac with 88% MMLU

JANGQ-AI's proprietary quantization method proves that aggressive compression doesn't necessarily destroy coherence in Mixture-of-Experts models.

• –Standard 2-bit MLX quants of 30B models are typically unusable, making this 10GB footprint a major breakthrough for local AI on base-model Apple Silicon.
• –Nemotron-Cascade-2's architecture—activating only 3B out of 30B parameters—makes it exceptionally well-suited for this level of extreme quantization while preserving deep reasoning capabilities.
• –The local LLM community's attention is already splitting, with the post teasing larger 30-40GB and 60-70GB variants of a highly anticipated "Mistral 4" dropping later today.