The small number in an MoE announcement is tempting. Kimi K3.1 Pro lists 2.8 trillion total parameters and 104 billion active parameters . It’s easy to read that second figure as the amount of model you need to store. It isn’t. [1]

I worked through the weight arithmetic because that distinction changes a deployment discussion immediately. At an ideal four bits per parameter, the full payload is 1,400 GB . The selected 104B subset works out to 52 GB . Both calculations are correct. They answer different questions. 🧮

Computed payloads from the reported parameter counts. These are arithmetic scenarios, excluding metadata and runtime allocations; no Kimi checkpoint was loaded.

The experts you didn’t select still exist

Mixture of Experts, or MoE, routes a token through a selected subset of expert networks. That reduces the work associated with evaluating every expert for every token. It doesn’t erase the other experts’ learned weights.

Think of a workshop with many specialist benches.…