Estimate Fine-Tuning Memory Requirements

# Estimate Fine-Tuning Memory Requirements Explain how to estimate accelerator memory for language-model fine-tuning. Account for parameters, gradients, optimizer states, activations, temporary buffers, precision, sequence shape, and the chosen data, tensor, or pipeline parallel strategy. ### Constraints & Assumptions - Give a formula with named assumptions rather than a universal bytes-per-parameter claim. - Distinguish full fine-tuning from parameter-efficient adaptation. - Peak memory, not only steady-state allocation, determines feasibility. ### Clarifying Questions to Ask - Which optimizer and numeric formats are used? - How many parameters are trainable? - What batch size, sequence length, checkpointing, and parallelism are planned? ```hint Build a memory ledger Estimate persistent model state separately from shape-dependent activations and measured runtime overhead. ``` ### What a Strong Answer Covers - Parameter, gradient, master-weight, and optimizer-state accounting. - Activation scaling with layers, tokens, hidden size, microbatch, and checkpointing. - How sharding and parallelism redistribute rather than always eliminate memory. - Headroom, fragmentation, communication buffers, profiling, and validation. ### Follow-up Questions 1. How does LoRA change memory without shrinking frozen-model inference cost? 2. Why can a theoretically fitting model still run out of memory?