Making PyTorch & Local ML Work on Modest Hardware: Lessons from a 4GB/8GB Laptop GPU

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Memory impact: Your GPU only ever holds the activations and intermediate tensors for batch_size = 16, but the optimizer trajectory matches batch_size = 64. 2. Halve Activation Memory with Mixed Precision (torch. amp) PyTorch defaults to 32-bit floating point (FP32) for tensor allocations. Using native Automatic Mixed Precision (torch. amp) automatically casts non-sensitive operations into 16-bit while keeping sensitive weight calculations in 32-bit: Memory impact: Memory overhead for layer activations drops by roughly 40% to 50%, often allowing you to double your local micro-batch size without OOM errors.

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