Training large models on a budget?

Look into gradient checkpointing and mixed precision training. These can significantly reduce memory usage.

Also consider using cloud spot instances or preemptible VMs. They're much cheaper but require fault-tolerant training setups.

Model parallelism and data parallelism can help distribute the computational load across multiple GPUs.

Don't forget about quantization techniques - they can significantly reduce model size and inference time.