Estimate Fine-Tuning Memory Requirements
VoleonMachine Learning Engineer · Technical Screen
Last updated
Learn to estimate full and parameter-efficient fine-tuning memory with a transparent ledger for model state, activations, sharding, and peak overhead.
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?
Hints
Hint 1
Build a memory ledger: Estimate persistent model state separately from shape-dependent activations and measured runtime overhead.
Follow-up questions
- How does LoRA change memory without shrinking frozen-model inference cost?
- Why can a theoretically fitting model still run out of memory?
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