  In the complex domain of large language models (LLMs), striking a balance
between computational efficiency and maintaining model quality is a formidable
challenge. Navigating the inherent limitations of uniform quantization,
particularly when dealing with outliers, and motivated by the launch of
NVIDIA's H100 hardware, this study delves into the viability of floating-point
(FP) quantization, particularly focusing on FP8 and FP4, as a potential
solution. Our comprehensive investigation reveals that for LLMs, FP8 activation
consistently outshines its integer (INT8) equivalent, with the performance edge
becoming more noticeable in models possessing parameters beyond one billion.
For weight quantization, our findings indicate that FP4 exhibits comparable, if
not superior, performance to INT4, simplifying deployment on FP-supported
hardware like H100. To mitigate the overhead from precision alignment caused by
the disparity between weights and activations, we propose two scaling
constraints for weight quantization that negligibly impact the performance
compared to the standard W4A8 model. We additionally enhance our quantization
methods by integrating the Low Rank Compensation (LoRC) strategy, yielding
improvements especially in smaller models. The results of our investigation
emphasize the immense potential of FP quantization for LLMs, paving the way for
high-efficiency deployment in resource-limited settings.
