With newer models getting larger (especially in LLMs and multimodal setups), memory constraints are becoming a major bottleneck during training and inference.
Looking for practical approaches others are using to manage this, such as:
Would be useful to understand what’s working in real-world implementations and where trade-offs are being made.
Avatar Memory optimization in large-scale deep learning is mostly about reducing footprint without sacrificing too much performance.
I usually start with mixed precision training (FP16/BF16) to cut memory usage almost in half. Then use gradient checkpointing, which trades a bit of compute for significantly lower memory by recomputing activations during backprop.
For large models, model/data parallelism (like sharding weights across GPUs) is essential. I also keep batch sizes adaptive and use gradient accumulation to simulate larger batches without exceeding memory limits.
On top of that, techniques like pruning, quantization, and efficient architectures help reduce overall model size, while careful data pipeline handling prevents unnecessary memory overhead.
