In practice the fairest comparison would be to a dense ~8B model. Qwen Coder 30B A3B is a good sparse comparison point as well.
When people talk about sparse or dense models, are they spare or dense matrices in the conventional numerical linear algebra sense? (Something like a csr matrix?)
They compared it to GPT OSS 120B, which activates 5.1B parameters per token. Given the size of the model it's more than fair to compare it to Qwen3 32B.
Only if 120B fits entirely in the GPU. Otherwise, for me, with a consumer GPU that only has 32 GB VRAM, gpt-oss 120B is actually 2 times slower than Qwen3 32B (37 tok/sec vs. 65 tok/sec)
In the case of gpt-oss 120B that would means sqrt(5*120)=24B.
That's actually in line with what I had (unscientifically) expected. Claude Sonnet 4 seems to agree:
> The most accurate approach for your specific 120B MoE (5.1B active) would be to test it empirically against dense models in the 10-30B range.
1) performance constrained. like NVidia Spark with 128GB or AGX with 64GB.
2) memory constrained. like consumers' GPUs.
In first case MoE is clear win. They fit and run faster. In second case dense models will produce better results. And if performance in token/sec is acceptable then they are better choice.