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 →

Model configuration

Import a Hugging Face model profile.

Load the public model configuration to set parameters, attention shape, and available context in this plan.

Hugging Face model ID

Model parameters (B)

Training path

Model architecture

Adapter target modules

LoRA rank

Sequence length (tokens)

Micro-batch

Activation path

Training strategy

VRAM per GPU (GB)

Training GPUs

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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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.

Related path

Set sequence and batch →