Dynamic Precision as Digital Allostery: Reinterpreting MoE Inference Through Biochemical Logic

The migration of Large Language Models from data centers to edge devices represents one of the most pressing engineering challenges in contemporary machine learning. While Mixture of Experts architectures promise computational efficiency through sparse activation, they introduce a paradoxical constraint: the inactive parameter set creates a memory footprint that dwarfs available edge resources. Mixtral 8×7B, for instance, requires approximately 87 GB in BF16 format, placing it far beyond the capacity of consumer laptops or embedded accelerators. Traditional remedies such as uniform quantization or static offloading impose rigid latency accuracy trade-offs that fail to accommodate the fluid dynamics of real time inference. In their recent work, **DyMoE: Dynamic Expert Orchestration with Mixed-Precision