Paper: A Comprehensive Evaluation of Quantization Strategies for Large Language Models

Yesterday I read the paper – A Comprehensive Evaluation of Quantization Strategies for Large Language Models https://arxiv.org/pdf/2402.16775.

Quantization reduces the memory footprint of models by representing weights and activations in lower-precision formats (e.g., 8-bit integers instead of 16- or 32-bit floats). The paper discusses two approaches:

1. Quantization-Aware Training (QAT): Quantization is integrated into the training phase, helping models adapt to lower precision.
2. Post-Training Quantization (PTQ): Quantization is applied after training. PTQ is more common because it avoids the high retraining costs of QAT, despite potential performance trade-offs.

Some key takeaways:

• 4-bit quantization strikes a good balance between performance and efficiency.
• Performance drops become noticeable at 3 bits or fewer, and at 2 bits, models often fail to produce coherent outputs.
• Perplexity serves as a reliable benchmark for evaluating quantized models, showing that 8-bit models closely match their full-precision counterparts, with acceptable trade-offs at 4 bits.

While quantization saves memory, it can reduce inference speed, making it ideal for scenarios where memory is constrained but speed is less critical.

This paper offers practical insights for anyone optimizing LLMs for real-world applications.