LLM Full Fine-Tuning vs. LoRA / QLoRA Training VRAM Calculator

Estimate optimizer states, gradients, base weights, and activation VRAM for LoRA/QLoRA.

LoRA / QLoRA Training VRAM Required
20.09 GB
Frozen Base Model Weights 7 GB
Adapter Gradients + AdamW States 3.36 GB
Full FP16 Fine-Tuning Comparison 252 GB
Inputs
B params
Base LLM parameter count in billions.
bits
Frozen base model precision in bits (4 for QLoRA, 16 for LoRA).
%
Percentage of trainable LoRA adapter parameters (%).
tokens
Training sequence token length.
Results & Graph
LoRA / QLoRA Training VRAM Required
20.09 GB
Frozen Base Model Weights
7 GB
Adapter Gradients + AdamW States
3.36 GB
Full FP16 Fine-Tuning Comparison
252 GB

LoRA / QLoRA Training VRAM Required

Graph 1 • Base Model Billions Sensitivity Recalculates the domain equation across 50% to 150% of the primary input while holding other parameters constant.
0 GB 14.8 GB 29.6 GB 50%: 12.06 GB 12.1 50% 75%: 16.19 GB 16.2 75% 100%: 20.09 GB 20.1 100% 125%: 23.83 GB 23.8 125% 150%: 27.45 GB 27.4 150%
Graph 2 • Primary & Supporting Metric Comparison Compares the primary output alongside supporting domain benchmarks.
LoRA / QLoRA Training VRAM Required 20.09 GB Frozen Base Model Weights 7 GB Adapter Gradients + AdamW States 3.36 GB Full FP16 Fine-Tuning Comparison 252 GB
Graph 3 • Exact Output Table & Units Displays each calculated output in its exact domain unit.
LoRA / QLoRA Training VRAM Required20.09 GBFrozen Base Model Weights7 GBAdapter Gradients + AdamW States3.36 GBFull FP16 Fine-Tuning Comparison252 GB
Summary: Training a 1.5% adapter on a 14B (4-bit) base model requires ~20.09 GB VRAM vs. ~252 GB for full FP16 AdamW fine-tuning.
Summary: Training a 1.5% adapter on a 14B (4-bit) base model requires ~20.09 GB VRAM vs. ~252 GB for full FP16 AdamW fine-tuning.
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Formula & Mathematical Methodology: LoRA & QLoRA VRAM Systems Engineering Formula

QLoRAVRAMGB ≈ BaseParamsB × 0.55 + TrainableAdapterRatio × BaseParamsB × 16 + ActivationBuffer
QLoRA_VRAM_GB ≈ BaseParams_B × 0.55 + TrainableAdapterRatio × BaseParams_B × 16 + ActivationBuffer
  • baseModelBillions: Base Model Billions (B params)
  • baseWeightBits: Base Weight Bits (bits)
  • trainableAdapterPct: Trainable Adapter Pct (%)
  • sequenceLengthTokens: Sequence Length Tokens (tokens)

Rounding: Binary/decimal capacities and engineering metrics retain full floating-point precision and round to 2 decimal places.

Worked Example: Architecture Sizing Example: LoRA & QLoRA VRAM

Estimate optimizer states, gradients, base weights, and activation VRAM for LoRA/QLoRA.

  1. Enter the workload parameters, model dimensions, or network link specifications.
  2. Compute the sizing equation: QLoRA_VRAM_GB ≈ BaseParams_B × 0.55 + TrainableAdapterRatio × BaseParams_B × 16 + ActivationBuffer.
  3. Inspect the primary capacity metric, headroom buffers, and 3 SVG visualizations.
Result: Calculates LoRA & QLoRA VRAM via QLoRA_VRAM_GB ≈ BaseParams_B × 0.55 + TrainableAdapterRatio × BaseParams_B × 16 + ActivationBuffer.

Frequently Asked Questions

How does the LLM Full Fine-Tuning vs. LoRA / QLoRA Training VRAM Calculator compute capacity?

It evaluates QLoRA_VRAM_GB ≈ BaseParams_B × 0.55 + TrainableAdapterRatio × BaseParams_B × 16 + ActivationBuffer directly in your browser and plots three custom SVG diagrams.

Can I compare different cluster or model configurations side by side?

Yes. Use the "Compare (0/3)" button in the results panel to pin up to three configurations.

Does this tool upload any of my architecture parameters?

No. All calculations run 100% client-side in your browser.

Developer Tools · AI & LLM Infrastructure

LLM Full Fine-Tuning vs. LoRA / QLoRA Training VRAM Calculator: inputs, method, and how to read the result

Estimate optimizer states, gradients, base weights, and activation VRAM for LoRA/QLoRA.

Choose inputs for the case you are checking

The defaults are illustrative. Replace them with values for your case and keep the units shown beside each field.

  • Base Model Billions: Base LLM parameter count in billions.
  • Base Weight Bits: Frozen base model precision in bits (4 for QLoRA, 16 for LoRA).
  • Trainable Adapter Pct: Percentage of trainable LoRA adapter parameters (%).
  • Sequence Length Tokens: Training sequence token length.

What the calculation includes

With the default values shown, LoRA / QLoRA Training VRAM Required is 20.09 GB. The calculation follows LoRA & QLoRA VRAM Systems Engineering Formula; the formula and variable definitions above show how the inputs combine.

One-input check: With the other defaults held constant, changing Base Model Billions from 14 B params to 15 B params changes LoRA / QLoRA Training VRAM Required from 20.09 GB to 21.17 GB. This is a comparison inside the calculator's stated model, not a prediction.

Where to use caution

  • Uses industry-standard IEEE, IETF RFC, and GPU/cloud hardware sizing conventions.
  • Capacity planning estimate; benchmark under production traffic patterns before finalizing cluster or hardware procurement.

Scope: IEEE / IETF / Cloud Systems Engineering Standard Check the assumptions and limitations against your situation before relying on the estimate.

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