arXiv:2411.02355v4 Announce Type: replace Abstract: Quantization is a powerful tool for accelerating large language model (LLM) inference, but the accuracy-performance trade-offs across different formats remain unclear. In this paper, we conduct the most comprehensive empirical study to date, evaluating FP8, INT8, and INT4 quantization across academic benchmarks and real-world tasks on the entire Llama-3.1 model family. Through over 500,000 evaluations, our investigation yields several key findings: (1) FP8 (W8A8-FP) is effectively lossless across all model scales, (2) well-tuned INT8 (W8A8-IN
The rapid growth of LLMs demands more efficient inference, making quantization research critical for practical deployment and scaling computation.
Understanding the accuracy-performance trade-offs in LLM quantization directly impacts the cost and accessibility of advanced AI models for enterprises and research alike.
This comprehensive study provides clearer guidance on choosing optimal quantization formats, potentially accelerating the deployment of smaller, faster, yet still highly capable LLMs.
- · AI developers
- · Cloud providers
- · Edge AI hardware manufacturers
- · LLM application developers
- · Companies with inefficient model deployment strategies
Reduced computational costs and energy consumption for LLM inference.
Democratization of advanced AI capabilities through more accessible model sizes.
Increased proliferation of specialized or embedded LLM applications in cost-sensitive environments.
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Read at arXiv cs.LG