How does model quantization impact ChatGPT inference architecture and quality trade-offs?

Quantization reduces model precision to speed up inference and reduce memory, but may slightly degrade output quality.

Quantization in ChatGPT-style systems converts high-precision floating-point weights (FP32/FP16) into lower precision formats like INT8 or FP8. This reduces memory footprint and improves inference speed due to faster matrix operations and better hardware utilization.

Architecturally, quantization is applied to weights, activations, or both. Post-training quantization (PTQ) is faster but less accurate, while quantization-aware training (QAT) integrates precision loss during training for better quality retention.…