Provides the capacity of a 100B+ model while activating only a subset of experts per token. This approach enables cost-efficient scaling for applications that need richer world knowledge and more nuanced outputs than smaller Llama variants. The model targets “intelligence at scale” scenarios such as analytics assistants, complex chat agents, and knowledge management tools where throughput matters. It’s well-positioned as a mid-high tier engine for organizations that want MoE benefits without the cost of ultra-large dense models.
