A preprint examines combinations of shared scales, weights and activations in MX-like low-precision formats for large language model inference. The researchers jointly explore model accuracy and estimated silicon trade-offs when integrating a tensor core with an edge neural processing unit.

The study frames its findings as a hardware-software design-space exploration to identify promising configurations. It does not announce a shipping chip; results depend on the models, quantization settings and hardware assumptions in the paper.