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LLM VRAM Fit Estimator — result sheet
Estimate GPU memory for loading a language model from parameter count and quantization, and see which cards it fits.
Inputs used
Results
Visual chart
Breakdown
Calculation steps
Returned data table
Formula and methodology
Formula: Weights GB = billions × bits / 8. Total = weights × (1 + overhead/100).
Each parameter stored at the quantized bit-width contributes bits/8 bytes. Runtime overhead for context and KV cache sits on top. The verdict lists common card sizes the total fits within.
This result follows the calculator's declared inputs, precision, validation boundaries, and model limits.
Input contract
- Parameters — billions; minimum 0.01; maximum 100000
- Quantization — 7 choices
- Runtime overhead — %; Context, KV cache, and framework overhead on top of weights.; minimum 0; maximum 500
Worked example
| Input | Value |
|---|---|
| Parameters | 70 |
| Quantization | 4 |
| Runtime overhead | 20 |
Weights 35 GB; total 42 GB; fits 48 GB and larger cards.
Assumptions and limits
- Decimal gigabytes (10^9 bytes).
- Overhead is a single user percentage, not a measured context profile.
- MoE active-expert routing and offloading are not modeled.
Calculator note
Source and methodology
Use the official WorldCalculate methodology policy for the source, formula, precision, and boundary standards behind this calculator.
Planning estimate, not financial, medical, legal, or professional advice. © WorldCalculate — reuse with attribution. Built and curated by Hassan ALRowaie.
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