Does size matter?
Memory requirements are the most obvious advantage of reducing the complexity of a model’s internal weights. The BitNet b1.58 model can run using just 0.4GB of memory, compared to anywhere from 2 to 5GB for other open-weight models of roughly the same parameter size.
But the simplified weighting system also leads to more efficient operation at inference time, with internal operations that rely much more on simple addition instructions and less on…
Article Source
https://arstechnica.com/ai/2025/04/microsoft-researchers-create-super‑efficient-ai-that-uses-up-to-96-less-energy/
