LoRA training planner

Plan the training pressure before the training run.

Compare LoRA and QLoRA from frozen base weights, adapter target scope, sequence length, micro-batch, checkpointing, and the per-GPU effect of single, DDP, or FSDP training.

LoRA and QLoRA planner

Make the training memory blocks visible before the run.

Plan frozen weights, adapters, optimizer state, activation load, sequence length, micro-batch, and distributed strategy together.

How the estimates work →

The values below are editable planning baselines. Change the few inputs you know; open Advanced assumptions only when they change your decision.

Model configuration

Import a Hugging Face model profile.

Load a public model revision to set its verified parameter total and decoder attention shape. The current workload context stays unchanged.

Current hardware

No hardware profile saved.

Save the GPU capacity you already have, then apply it across VRAM planning paths.

Advanced assumptionsAdjust architecture, adapter shape, activation policy, and distributed strategy after the workload is fixed.
Frozen base / GPU——
Adapter and optimizer / GPU——
Activation load / GPU——
Training capacity——

Planning brief

Adjust the batch or sequence before the GPU target.

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Next decision

Verify the training shapeCompare LoRA and QLoRA

Plan tools

Keep this useful version close, shareable, and easy to revisit.

Reset inputs

Optional support

Keep practical planning tools available.

If this result made the next decision clearer, an optional contribution helps cover hosting, source reviews, and continued improvements.

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What it models

Frozen base weights, target-module adapter state, optimizer memory, activation load, runtime reserve, and usable VRAM per GPU.

Pressure control

Use sequence length, micro-batch size, checkpointing, adapter scope, mode selection, and training strategy as explicit levers.