🗜️ LLM Quantization Calculator
Estimate LLM storage and VRAM reduction from FP32, FP16, INT8, INT4, or custom-bit quantization, including metadata overhead.
Calculate LLM Quantization
What this LLM quantization calculator calculates
Compare a source precision against a target quantization precision and estimate raw model-size reduction, percentage saved, and effective bits per parameter.
Memory savings do not guarantee equal quality or speed. Kernel support, calibration, quantization method, and hardware determine the practical result.
LLM Quantization Calculator formula
Assumptions and limitations
The result is an engineering estimate based on the values entered. AI models, tokenizers, runtimes, accelerators, cloud services, and provider billing rules differ. Validate important decisions with measured data from the exact model, hardware, framework, and pricing plan you intend to use.
Worked example
Moving from FP16 to an effective 4.32 bits per parameter reduces raw weight storage by about 73%.
How to use the result
Memory savings do not guarantee equal quality or speed. Kernel support, calibration, quantization method, and hardware determine the practical result.
- Start with representative production assumptions rather than best-case demos.
- Run a low, expected, and high scenario to understand the range.
- Record model version, pricing date, hardware, precision, and workload details.
- Replace assumptions with observed p50 and p95 measurements after testing.
Common mistakes to avoid
- Assuming INT4 always gives exactly a 4× reduction from FP16.
- Ignoring quality loss, outlier handling, and calibration requirements.
- Expecting speedups when the hardware or runtime lacks optimized low-bit kernels.
Frequently asked questions
What is effective bits per parameter?
It includes the nominal quantization bits plus metadata such as scales and zero points.
Does quantization reduce KV-cache memory?
Weight quantization alone does not necessarily change KV-cache precision.
Which quantization level is best?
It depends on quality tolerance, hardware support, model architecture, and workload; benchmark on representative data.
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Methodology and privacy
This educational tool uses the formula and assumptions displayed on the page. Calculations run locally in your browser, and the page does not transmit the values you enter. Results are estimates rather than provider quotes, benchmark guarantees, financial advice, or capacity guarantees. Last methodology review: August 2, 2026.
Formula Explorer connections
Interpretation: This formula connects model architecture and numeric precision to compute, storage or accelerator-memory demand. Assumption: Architecture details, optimizer states, activations, framework buffers, sharding and quantization metadata can change measured requirements.