Why is LoRA so much cheaper than full fine-tuning?

LoRA trains a small number of adapter weights while freezing the base model, so far fewer gradients and optimizer states are computed. This slashes both VRAM and GPU hours, often making it 5-10x cheaper than updating every parameter.

What is the difference between LoRA and QLoRA?

QLoRA quantizes the frozen base model to 4-bit, cutting VRAM further so you can fine-tune large models on a single consumer or mid-range GPU. It is slightly slower per step but the lower memory footprint usually wins on cost.

How are GPU hours estimated?

The tool models training as roughly three passes (epochs) over your dataset, applies a per-method efficiency factor, and divides by the chosen GPU's effective token throughput. It is an order-of-magnitude estimate, not a benchmark.

Does cheaper always mean better?

No. Full fine-tuning can reach higher quality on large domain shifts, while LoRA/QLoRA are ideal for style, format and task adaptation. Use this tool to weigh cost against the depth of adaptation you actually need.

LoRA vs Full Fine-Tuning Cost Calculator

LoRA vs full fine-tuning cost calculator

Full fine-tuning updates every weight in the model — billions of gradients, optimizer states and a large VRAM bill. Parameter-efficient methods like LoRA and QLoRA freeze the base model and train tiny adapters instead. This calculator estimates the GPU hours, VRAM and dollar cost for each approach so you can pick the cheapest method that meets your quality bar.

How it works

Training cost scales with how much compute touches the model. The tool models a few epochs over your dataset and applies a method-specific efficiency factor — LoRA and QLoRA touch far fewer parameters than full fine-tuning, so they finish in a fraction of the GPU hours:

gpu_hours ≈ (dataset_tokens × epochs × method_factor) / gpu_throughput
cost      = gpu_hours × gpu_price_per_hour

VRAM is estimated from the model size and the method: full fine-tuning needs memory for weights plus gradients plus optimizer states (~16 bytes/param), while QLoRA’s 4-bit base slashes that requirement.

Tips and notes

Start with QLoRA — it usually matches LoRA quality for task adaptation at the lowest VRAM and cost, and runs on a single mid-range GPU.
Reserve full fine-tuning for large domain shifts where adapters can’t absorb enough new behaviour; for style and format tasks it’s overkill.
These are planning estimates. Real throughput depends on sequence length, batch size and framework, so validate on a small run before committing.