The primary challenge when applying quantization to any neural network, including LLMs, is the potential for accuracy degradation. This occurs because quantization reduces the precision of the model's weights and activations, which can lead to a loss of critical information. Embedding layers can be particularly sensitive to this precision loss. Quantization-Aware Training (QAT) is a standard and effective technique to mitigate this issue. QAT simulates the effects of low-precision arithmetic during the training or fine-tuning process, allowing the model to adapt its parameters to be more robust to the quantization errors, thereby minimizing the accuracy drop.

A. Quantization's primary goal and effect is to reduce memory usage and computational cost, not increase it. The premise of this option is fundamentally incorrect.

B. While skipping quantization for sensitive layers (mixed-precision) is a valid technique, it is a workaround that avoids the problem. QAT is a more direct mitigation that improves the model's tolerance to quantization.

D. The statement that LLMs are "inherently resistant" is an oversimplification. While challenging, LLMs can be effectively quantized. Proposing to switch to a smaller model is an alternative, not a mitigation technique for quantizing a large model.

1. Gholami

A.

Kim

S.

Dong

Z.

et al. (2022). A Survey of Quantization Methods for Efficient Neural Network Inference. In Low-Power Computer Vision (pp. 291-326). ACM.

Section 3.2

"Quantization-Aware Training (QAT)": This section details how QAT simulates quantization during training to allow the model to recover from the accuracy loss incurred by post-training quantization (PTQ). It states

"To compensate for the quantization loss

we can fine-tune the quantized network on the training data... This process is called Quantization-Aware Training (QAT)."

2. Frantar

E.

& Alistarh

D. (2023). GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

Section 1

"Introduction": The paper opens by stating the core problem: "A major issue is that standard quantization techniques can significantly degrade the accuracy of these large models... For instance

directly applying rounding-to-nearest to quantize all weights of OPT-175B to 8-bit integers results in a model that produces unusable outputs." This directly supports the premise that quantization can cause a significant accuracy drop.

3. PyTorch Official Documentation. (n.d.). Quantization. PyTorch. Retrieved from https://pytorch.org/docs/stable/quantization.html

"Quantization Aware Training" section: The documentation explicitly contrasts Post Training Quantization with QAT

noting

"Quantization aware training (QAT) is the quantization method that typically results in the highest accuracy... With QAT

all weights and activations are 'faked' quantized during the forward and backward passes of training." This confirms QAT as a high-accuracy mitigation strategy.