They're comparing Qwen's moe vs dense (smaller difference) against Gemma's moe vs dense (bigger difference). Your proposed alternative misses the point.

Dense is (much) worse in terms of training budget. At inference time, dense is somewhat more intelligent per bit of VRAM, but much slower, so for a given compute budget it's still usually worse in terms of intelligence-per-dollar even ignoring training cost. If you're willing to spend more you're typically better off training and running a larger sparse model rather than training and running a dense one.

Dense is nice for local model users because they only need to serve a single user and VRAM is expensive. For the people training and serving the models, though, dense is really tough to justify. You'll see small dense models released to capitalize on marketing hype from local model fans but that's about it. No one will ever train another big dense model: Llama 3.1 405B was the last of its kind.

MoE isn't inherently better, but I do think it's still an under explored space. When your sparse model can do 5 runs on the same prompt in the same time as a dense model takes to generate one, there opens up all sorts of interesting possibilities.