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:

• Gradient checkpointing vs mixed precision
• Model sharding or distributed training strategies
• Efficient data loading and batching

Would be useful to understand what’s working in real-world implementations and where trade-offs are being made.