Train Larger AI Models with Gradient Accumulation

Thu, 03 Sep 2026 00:00:00 +0700

Training a neural network often becomes memory-bound before it becomes compute-bound. You may want a batch of 64 examples for stable optimization, but the model, activations, optimizer state, and input tensors leave enough accelerator memory for only 8 examples at a time.

Reducing the batch size to 8 may work, but it also changes the optimization process. Gradient accumulation provides another option: process several smaller microbatches, add their gradients together, and update the model only after the desired effective batch has been processed.

Understand Knowledge Distillation for Smaller AI Models

Thu, 03 Sep 2026 00:00:00 +0700

A large model may produce useful predictions but still be too expensive or slow for the environment where it must run. A mobile application, an edge device, or a high-volume service can have tighter limits on memory, latency, and compute.

Knowledge distillation is one way to address that gap. Instead of training a smaller model only from the original labels, we also train it to imitate information produced by a stronger teacher model. The smaller model is called the student.

Model Training on Nalar

Train Larger AI Models with Gradient Accumulation

Understand Knowledge Distillation for Smaller AI Models