Batch Size

Batch size is the number of training examples processed together in one forward-backward pass before the optimizer updates the model weights, a fundamental hyperparameter affecting training speed, memory, and final model quality. LLM pretraining uses very large effective batch sizes — typically 1M to 4M tokens per step — to stabilize gradient estimates and exploit massive parallelism across GPUs. Fine-tuning uses much smaller batches, often 32 to 128 examples, where memory constraints and small dataset sizes limit batch size. Batch size interacts with learning rate — the linear scaling rule says doubling batch size approximately requires doubling learning rate to preserve training dynamics, though this breaks down at extreme scales. Gradient accumulation allows simulating larger batches than fit in memory by accumulating gradients over multiple forward passes before each optimizer step. Modern frameworks like DeepSpeed, FSDP, and Axolotl handle batch size, gradient accumulation, and distributed training transparently. AI governance teams document the effective batch size (per_device × num_devices × gradient_accumulation_steps) in their training lineage.

Batch-trained models in Centralpoint: Centralpoint routes to models trained with whatever batch configurations are appropriate to their scale and provider, all in a model-agnostic platform. Tokens are metered per skill, prompts stay local, supports generative and embedded models, and deploys chatbots through one line of JavaScript on any portal.


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