Large Batch Training › Practical Guidance: Putting It All Together

Moving from theory to a working large-batch training setup involves several steps that are easy to get wrong. Here is a practical checklist for configuring large batch training in a real project.

First, establish your baseline at a small batch size. Train your model at batch size 32 or 64 with the default learning rate for your optimizer. Verify that training converges stably and achieves the expected validation performance. This baseline is the reference you will compare everything against.

Second, choose your target batch size. This should be driven by your hardware: how many GPUs you have and how large a batch each can process. For multi-GPU training, the total batch is the per-GPU batch times the number of GPUs. Aim to hit the critical batch size approximately; going much higher wastes compute efficiency without improving convergence.

Third, apply the linear scaling rule. Multiply your baseline learning rate by $k = \text{target batch size} / \text{baseline batch size}$. Add a warmup schedule starting from $\eta' / k$ or a fixed small value, ramping to $\eta'$ over the first 5% of training steps.

Fourth, choose your optimizer. Use Adam for small to medium batch sizes and standard transformer architectures. Use LAMB when you are training transformers with batch sizes above 8,192 tokens or when you observe training instability at the linearly scaled learning rate.

Fifth, monitor training stability carefully. Large batch training with a scaled learning rate can diverge quickly if something is misconfigured. Monitor gradient norms, weight norms, and per-layer update norms in the first few hundred steps. If any layer shows explosive gradient growth or the loss spikes, reduce the learning rate or extend the warmup.

Finally, validate generalization. Run your large-batch model on the held-out test set and compare to the small-batch baseline. If there is a meaningful accuracy gap, consider adding data augmentation, ghost batch normalization, or a longer training schedule to compensate.