QAT — Quantization-Aware Training

Fact

A method that simulates low-bit math during training to reduce accuracy loss.

In Plain Words

QAT is like practicing a school play on a tiny cafeteria stage. When the show starts, the actors stop bumping into the piano.

During training, the model follows low-bit limits, so Quantization later hurts less. You see it in on-device AI and VRAM-saving work.

Related Concepts

• Quantization
• Fine-tuning
• Video RAM

Quantization
QAT prepares the model for Quantization, so accuracy drops less.

Fine-tuning
QAT often adds one extra training round to an existing model.

VRAM
QAT helps low-bit deployment, so the model can use less VRAM.