While your remark is valid, there's two small inaccuracies here:

> GPT-OSS-20B has 3.6B active parameters, so it should perform similarly to a 3-4B dense model, while requiring enough VRAM to fit the whole 20B model.

First, the token generation speed is going to be comparable, but not the prefil speed (context processing is going to be much slower on a big MoE than on a small dense model).

Second, without speculative decoding, it is correct to say that a small dense model and a bigger MoE with the same amount of active parameters are going to be roughly as fast. But if you use a small dense model you will see token generation performance improvements with speculative decoding (up to x3 the speed), whereas you probably won't gain much from speculative decoding on a MoE model (because two consecutive tokens won't trigger the same “experts”, so you'd need to load more weight to the compute units, using more bandwidth).

So, this is all true, but this calculation isn't that nuanced. It's trying to get you into a ballpark range, and based on my usage on my real hardware (if I put in my specs, since it's not in their hardware list), the results are fairly close to my real experience if I compensate for the issue where it's calculating based on total params instead of active.

So by doing so, this calculator is telling you that you should be running entirely dense models, and sparse MoE models that maybe both faster and perform better are not recommended.