Before it
Mixture-of-experts models were built and kept inside large labs.
The problem
Nobody outside those labs could study or deploy a sparse model.
What it introduced
An open sparse MoE, 47B total parameters with about 13B active per token, matching much larger dense models.
Why it matters
It brought MoE into the open ecosystem and made the total-versus-active parameter distinction common knowledge.
What modern models inherit
Sparse open models as a normal deployment option.
What came next
DeepSeekMoE, Llama 4, Kimi and the open MoE era.