What is Quantization
Quantization - Reducing model precision for efficient inference
A model optimization technique that reduces the numerical precision of model weights and activations from floating-point to lower-bit representations. Quantization dramatically reduces memory usage and inference latency for deploying multimodal AI models in production.
How It Works
Quantization maps continuous floating-point values (FP32) to discrete lower-precision values (INT8, INT4, or even binary). Post-training quantization applies this mapping after training using calibration data to determine appropriate scaling factors. Quantization-aware training simulates quantization during training so the model adapts to the lower precision. The result is a smaller, faster model with minimal accuracy loss.
Technical Details
Common precision levels include FP16 (2x compression), INT8 (4x), INT4 (8x), and binary (32x). Symmetric quantization uses a single scale factor while asymmetric adds a zero-point offset. GPTQ, AWQ, and GGUF are popular quantization methods for large language models. Hardware accelerators (GPU tensor cores, Intel VNNI) provide optimized kernels for quantized computation. Mixed precision uses different precision levels for different layers based on sensitivity.
Best Practices
- Start with INT8 post-training quantization as a baseline, which usually has minimal quality impact
- Use calibration data that represents the production data distribution for accurate scaling
- Apply mixed precision, using lower precision for less sensitive layers and higher for critical ones
- Benchmark both accuracy and latency to verify the quantized model meets requirements
Common Pitfalls
- Quantizing too aggressively (INT4 or lower) without checking for accuracy degradation
- Using calibration data that does not represent the actual data distribution
- Not testing quantized models on edge cases where precision loss may cause failures
- Assuming all layers are equally tolerant to quantization
Advanced Tips
- Use GPTQ or AWQ for efficient quantization of large language models to INT4 with minimal quality loss
- Quantize multimodal embedding models for faster inference in production retrieval systems
- Implement dynamic quantization that adapts precision based on input characteristics
- Combine quantization with distillation for maximum compression with minimized accuracy loss