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Data imbalance in MoE models is overcome by biasing expert selection, unlocking capacity for underrepresented categories without sacrificing cross-category sharing.
Training a 70B parameter open-source LLM on a supercomputer reveals the hidden engineering hurdles and infrastructure adaptations needed to democratize large-scale AI development beyond the private sector.
