Data & AI

Post-Training Quantization Flow (FP16 to INT4)

Data & AI Pipelines diagram · made in Skemio

Post-training quantization architecture showing FP16 weights and calibration data flowing through profiling, quantization, and bit-packing to INT4 inference.
Post-training quantization architecture showing FP16 weights and calibration data flowing through profiling, quantization, and bit-packing to INT4 inference.
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What it shows

  • Calibration sets compute activation ranges without retraining the model.
  • Weight clipping minimizes quantization error across critical layers.
  • INT4 bit packing shrinks VRAM footprint by 75% with under 1% loss.

The takeaway

PTQ lets you run 70B parameter models on single GPU instances with near-lossless output quality.

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