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Quantization-Aware Training
Train models that are robust to reduced numerical precision by simulating quantization effects during the forward pass, enabling deployment at INT4 or INT8 precision without accuracy loss. QAT inserts fake quantization nodes into the computation graph during training — the model learns to compensate for the information loss of lower precision, producing models that run 2-4x faster with 75% less memory while maintaining accuracy benchmarks.
Papers, code, and datasets
LLM.int8(): 8-bit Matrix Multiplication for Transformers at Scale
Read on arXiv ↗
QLoRA: Efficient Finetuning of Quantized Language Models
Read on arXiv ↗
AWQ: Activation-aware Weight Quantization for LLM Compression and Acceleration
Read on arXiv ↗
GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers
Read on arXiv ↗
SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
Read on arXiv ↗
Datasets & ModelsWant to explore this concept?
Whether you're evaluating quantization-aware training for your workflow or need help implementing it, we can help.