How This Calculator Works
AI Hardware turns the inputs into a visible formula-based estimate. Use the result as a planning check, then compare a lower, expected, and higher scenario when the input values are uncertain.
Use this local AI hardware calculator to estimate memory requirements for running language models locally based on model size, quantization, and workload.
The calculator estimates model memory from parameter count and precision, then adds context and operating overhead.
Formula
VRAM estimate = (parameters × bytes per parameter + context overhead) × buffer.
Example Calculation
An 8B model at Q4 with 2 GB overhead estimates roughly 7.2 GB VRAM before workload variation.
When to Use This Calculator
- Plan a local LLM build
- Compare quantization options
- Estimate GPU memory before buying hardware
Practical Scenarios
- Use the calculator before a decision depends on the number, then write down the inputs that would be easiest to verify. Use case: Plan a local LLM build.
- Rerun the estimate when the most uncertain input changes, so the result shows a useful range instead of one brittle answer. Start with AI Hardware, then compare the changed result with the original.
- Use the related calculators when the result affects a wider cost, schedule, or planning workflow. This is especially useful when you need to estimate gpu memory before buying hardware.
Tips
- Actual memory varies by runtime
- Context length can dominate memory
- Leave a buffer for the OS and model loader
Common Mistakes
- Using one unusually good input as if it were the normal case.
- Mixing units, time periods, or assumptions from different scenarios.
Assumptions and Limitations
The Local AI Hardware Calculator is most useful when every input belongs to the same real-world scenario, unit, and time period. Review the formula, assumptions, and related calculators before using the result in a decision.
- Local rules, fees, availability, timing, and real-world conditions can change the result.
- The result is an estimate and should be checked before making an important decision.
- Use realistic low, expected, and high scenarios when uncertainty matters.
AI Hardware uses AI hardware, local LLM, VRAM and GPU as the main context for the formula, example, and assumptions.