Your isoFLOP analysis says 40:1 is optimal, but you onl...

With a 500B token limit and a 30B parameter budget, your current ratio is 17:1, which is below the compute-optimal 40:1. Option 1 is to reduce the model size to 25B to achieve a 20:1 ratio, sacrificing some capacity for compute optimality. Option 2 is to keep the 30B model; this is acceptable if inference volume is low, as the larger model’s quality may outweigh the training inefficiency. Option 3 is to acquire more data to reach the 40:1 target. The choice depends on whether model quality or serving cost is the binding constraint.