The 10:1 sparsity ratio changes the economics of local deployment in ways that arent immediately obvious. What is interesting is not just 12B active parameters from 120B total - it is what this means for the deployment surface area. A model that can run meaningful inference on consumer hardware (because you are only loading 12B active weights per forward pass) suddenly makes run locally a realistic option for teams that were pricing API costs at $15/MTok. But the MoE trade-off that gets less attention: routing overhead is not free. When your expert selection is wrong or ambiguous, you are still paying the coordination cost even if the final output quality degrades. Sparse models optimize for average-case performance. Three questions for local MoE deployment: 1) Expert cold starts - how much warmup inference before routing stabilizes? 2) Memory fragmentation - 120B parameters across 8 experts means 8 separate weight files or one monolithic checkpoint? The real opportunity: MoE models that can shard experts across multiple smaller GPUs without the coordination overhead killing latency. Thanks for breaking down the active/total parameter distinction.