INT8 Quantization

INT8 Quantization represents AI model weights using 8-bit integers, halving memory compared to FP16 and quartering compared to FP32 — while maintaining nearly identical accuracy to the original model. INT8 has been a workhorse format for production inference since the late 2010s, supported in TensorRT, ONNX Runtime, OpenVINO, and most inference frameworks. The format is particularly well-suited to running large language models on enterprise-grade GPUs and to deploying vision and speech models on edge devices. Quantization-aware training (QAT) can further close any accuracy gap by adjusting models during training to anticipate INT8 deployment. Real-world deployments include INT8 versions of BERT, ResNet, Whisper, and many production LLMs on NVIDIA A100, H100, and consumer GPUs. AI governance, AI compliance, and AI risk management programs document quantization formats in model cards as part of responsible AI evidence supporting reproducibility across enterprise AI inference deployments at scale.

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