Quantization vs Pruning

Quantization — at a glance

Category: Neural Networks · Difficulty: Advanced

Reducing numerical precision of model weights (e.g., 32-bit to 4-bit) to shrink size and speed up inference.

Quantization maps high-precision values to fewer discrete levels. INT8 halves memory vs FP16 with negligible quality loss. INT4 (GPTQ, AWQ) enables running 70B models on consumer GPUs. Methods include post-training quantization and quantization-aware training.

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Pruning — at a glance

Category: Neural Networks · Difficulty: Advanced

Removing redundant weights or neurons from a network to reduce size and improve inference speed.

Magnitude pruning removes weights near zero. Structured pruning removes entire neurons or heads. The Lottery Ticket Hypothesis suggests dense networks contain sparse subnetworks matching full performance. Pruning can achieve 50-90% sparsity with minimal accuracy loss.

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Key differences

• Purpose: Quantization is typically used for neural networks problems, while Pruning fits neural networks use cases.
• Complexity: Quantization is rated Advanced; Pruning is rated Advanced.
• Definitions: Reducing numerical precision of model weights (e.g., 32-bit to 4-bit) to shrink size and speed up inference. vs Removing redundant weights or neurons from a network to reduce size and improve inference speed.

Frequently asked questions

What is the difference between Quantization and Pruning?

Quantization: Reducing numerical precision of model weights (e.g., 32-bit to 4-bit) to shrink size and speed up inference. Pruning: Removing redundant weights or neurons from a network to reduce size and improve inference speed.

When should I use Quantization instead of Pruning?

Use Quantization when your problem matches its strengths: Reducing numerical precision of model weights (e.g., 32-bit to 4-bit) to shrink size and speed up inference. Use Pruning when Removing redundant weights or neurons from a network to reduce size and improve inference speed.

Can Quantization and Pruning be used together?

Yes — many modern AI systems combine Quantization and Pruning to get the strengths of both approaches.

Is Quantization better than Pruning?

Neither is universally better. The right choice depends on data, latency, cost, and task. This page breaks down the trade-offs.