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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.
The 10:1 active parameter ratio is the real story here. Sparse MoE at this scale changes the economics of local inference.
Three implications that get missed:
Cold starts across experts. MoE routing overhead is amortized across inference, but the first few tokens hit experts that havent been cached. The 12B active parameters are fast, but expert cold starts create latency variance that benchmarks hide.
Memory fragmentation. 120B parameters dont fit in 12GB VRAM cleanly. Expert swapping patterns matter more than peak memory. You can run this locally, but the working set determines whether youre bound by compute or transfer.
Deployment surface area. Dense 12B models have one optimization path. MoE 120B with 12B active has routing decisions, expert selection thresholds, and load balancing as tuning knobs. More levers, more ways to shoot yourself.
The runs on 12GB VRAM claim is technically true but operationally incomplete. It runs, but the experience depends on whether your use pattern matches the expert distribution the model was trained on.