Executive Summary

Using a dataset of 470 models of graphics processing units (GPUs) released between 2006 and 2021, we find that the amount of floating-point operations/second per $ (hereafter FLOP/s per $) doubles every ~2.5 years. For top GPUs, we find a slower rate of improvement (FLOP/s per $ doubles every 2.95 years), while for models of GPU typically used in ML research, we find a faster rate of improvement (FLOP/s per $ doubles every 2.07 years). GPU price-performance improvements have generally been slightly slower than the 2-year doubling time associated with Moore’s law, much slower than what is implied by Huang’s law, yet considerably faster than was generally found in prior work on trends in GPU price-performance. Our work aims to provide a more precise characterization of GPU price-performance trends based on more or higher-quality data, that is more robust to justifiable changes in the analysis than previous investigations.

Figure 1. Plots of FLOP/s and FLOP/s per dollar for our dataset and relevant trends from the existing literature

Trend	2x time	10x time	Metric
Our dataset (n=470)	2.46 years [2.24, 2.72]	8.17 years [7.45, 9.04]	FLOP/s per dollar
ML GPUs (n=26)	2.07 years [1.54, 3.13]	6.86 years [5.12, 10.39]	FLOP/s per dollar
Top GPUs (n=57)	2.95 years [2.54, 3.52]	9.81 years [8.45, 11.71]	FLOP/s per dollar
Our data FP16 (n=91)	2.30 years [1.69, 3.62]	7.64 years [5.60, 12.03]	FLOP/s per dollar
Moore’s law	2 years	6.64 years	FLOP/s
Huang’s law	1.08 years	3.58 years	FLOP/s
CPU historical (AI Impacts, 2019)	2.32 years	7.7 years	FLOP/s per dollar
Bergal, 2019	4.4 years	14.7 years	FLOPs/dollar

Table 1. Summary of our findings on GPU price-performance trends and relevant trends in the existing literature with the 95% confidence intervals in square brackets.

In future work, we intend to build on this work to produce projections of GPU price-performance, and investigate how our findings inform us about the growth in dollar-spending on computing hardware in Machine Learning.

We would like to thank Alyssa Vance, Ashwin Acharya, Jessica Taylor and the Epoch team for helpful feedback and comments.

zheng tianfeng

zheng tianfeng

zheng tianfeng

zheng tianfeng