Ensemble methods improve prediction quality by combining multiple independent models. While effective, ensembles are rarely deployed in production AI inference due to the memory cost of loading multiple models simultaneously. The Apple M2 Ultra with 192GB unified memory changes this calculus: we can load 2-3 70B-parameter models at 4-bit quantization (~40GB each) alongside a routing ensemble layer, with memory remaining for KV cache. We propose Collective Voting, an inference framework that runs
1@techreport{hayulalab2026collectivevoting,
2 title={Collective Voting: Multi-Model Ensemble Inference on Unified Memory — Hayula Research},
3 author={Hayula AI Lab},
4 year={2026},
5 url={https://huggingface.co/hayulalab/collective-voting-paper}
6}
