You have 1023 FLOPs of training budget. How many parame...

To determine the optimal parameter count, start with the Chinchilla scaling relationship C ≈ 6ND. You must first establish the expected inference volume, as this dictates the trade-off between training compute and inference efficiency. For a fixed budget, N = √C/120 provides the Chinchilla-optimal parameter count. However, if the model will be served heavily, you should aim for a model size an order of magnitude smaller to optimize for inference costs. Finally, consider the memory step function, as model size impacts hardware memory constraints during deployment.