Model Pruning Calculator

Estimate compression ratio, accuracy loss, and inference speedup

Original Model Size (Params)

7.0 B

Target Sparsity (%)

50%

Pruning Method

Magnitude Pruning

Removes smallest weight values. Simple and widely used. Flexible sparsity patterns.

Remaining Parameters

3.5 B

50% of original

Compression Ratio

2.0x

Model size reduction

Estimated Accuracy Drop

1.2%

Empirical estimate

Inference Speedup

1.5x

Actual speedup depends on hardware

Detailed Metrics

Model Size Reduction 50%

Inference Speedup Potential 1.5x

Risk of Accuracy Drop Medium

Recommendation

With 50% sparsity using magnitude pruning, you can expect a 2x compression ratio with minimal accuracy loss. Consider fine-tuning after pruning to recover any accuracy degradation.

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