Formula Used
Raw model size: parameters × bits per parameter ÷ 8.
Compressed size: effective weight size × compression ratio.
Deployment package: weights + overhead + assets + sharding, then safety margin.
Checkpoint size: weights + gradients + optimizer states + optional training states.
How to Use
- Select parameter-based or layer-based calculation.
- Enter parameter counts and model precision.
- Add compression, pruning, framework, and asset details.
- Configure optimizer states and checkpoint options.
- Choose units, storage cost, and rounding.
- Submit the form and export the results.
Example Data
| Example | Parameters | Precision | Approximate Raw Size |
|---|---|---|---|
| Small neural network | 1,000,000 | FP32 | 3.81 MiB |
| BERT Base | 110,000,000 | FP32 | 419.62 MiB |
| 7B model | 7,000,000,000 | FP16 | 13.04 GiB |
| 7B quantized model | 7,000,000,000 | INT4 | 3.26 GiB |
Precision and Deployment Guidance
FP32 preserves broad numerical range and training compatibility. FP16 and BF16 reduce memory significantly. INT8 and INT4 target efficient inference deployments.
Framework files include metadata and serialization structures. Tokenizers can dominate very small model packages. Always inspect the final exported artifact.
Pruning saves storage only with sparse representations. Compression ratios vary by weight distribution. Accuracy testing remains essential after optimization.
Frequently Asked Questions
1. Why is the actual file larger?
Serialization adds headers, metadata, alignment, and framework-specific structures.
2. Does pruning always reduce file size?
No. Dense storage keeps zero values unless sparse encoding is used.
3. How large are Adam optimizer states?
Adam commonly stores two extra state tensors per trainable parameter.
4. Why include FP32 master weights?
Mixed-precision training may retain FP32 copies for stable updates.
5. What is a safe compression ratio?
It depends on format and data. Conservative estimates are usually safer.
6. Are GGUF and SafeTensors overheads fixed?
No. Metadata, alignment, tensor count, and implementation affect overhead.
7. How are embedding layers counted?
Multiply vocabulary size by embedding dimension for the parameter count.
8. Can layers use different precisions?
Yes. Layer mode calculates each layer using its selected precision.
9. Why estimate several checkpoints?
Training systems often retain multiple checkpoints for recovery and comparison.