How does TensorFlow handle memory management for large models?

TensorFlow uses memory pooling and device allocation strategies to manage large model memory.

TensorFlow allocates memory in chunks on GPUs to reduce fragmentation. It also uses graph-level optimizations to reuse tensors and avoid redundant allocations. Large models require careful batch sizing and gradient checkpointing.