NVIDIA GPUs Performace Comparison (value per 1$)-拾光赋

Data Source
Code
Result

<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span>import</span> <span>matplotlib.pyplot</span> <span>as</span> <span>plt</span>
<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span>import</span> <span>dataframe_image</span> <span>as</span> <span>dfi</span>
<span>plt</span><span>.</span><span>rcParams</span><span>[</span><span>"figure.dpi"</span><span>]</span> <span>=</span> <span>300</span>
<span>plt</span><span>.</span><span>rcParams</span><span>[</span><span>"savefig.dpi"</span><span>]</span> <span>=</span> <span>300</span>
<span>plt</span><span>.</span><span>rcParams</span><span>[</span><span>"figure.figsize"</span><span>]</span> <span>=</span> <span>[</span><span>12</span><span>,</span> <span>5</span><span>]</span>
<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>()</span>
<span>df</span><span>[</span><span>'name'</span><span>]</span> <span>=</span> <span>[</span><span>'GeForce RTX 4090'</span><span>,</span> <span>'GeForce RTX 4080'</span><span>,</span> <span>'GeForce RTX 4070 Ti'</span><span>,</span> <span>'GeForce RTX 4070'</span><span>,</span> <span>'GeForce RTX 3090 Ti'</span><span>,</span> <span>'GeForce RTX 3090'</span><span>,</span> <span>'GeForce RTX 3080 Ti'</span><span>,</span> <span>'GeForce RTX 3080'</span><span>,</span> <span>'GeForce RTX 3070 Ti'</span><span>,</span> <span>'GeForce RTX 3070'</span><span>,</span> <span>'GeForce RTX 3060 Ti'</span><span>,</span> <span>'GeForce RTX 3060'</span><span>]</span>
<span>df</span><span>[</span><span>'release_date'</span><span>]</span> <span>=</span> <span>[</span><span>'October 12, 2022'</span><span>,</span> <span>'November 16, 2022'</span><span>,</span> <span>'January 5, 2023'</span><span>,</span> <span>'April 13, 2023'</span><span>,</span> <span>'March 29, 2022'</span><span>,</span> <span>'September 24, 2020'</span><span>,</span> <span>'June 3, 2021'</span><span>,</span> <span>'January 27, 2022'</span><span>,</span> <span>'June 10, 2021'</span><span>,</span> <span>'October 29, 2020'</span><span>,</span> <span>'October 27, 2022'</span><span>,</span> <span>'September 1, 2021'</span><span>]</span>
<span>df</span><span>[</span><span>'price'</span><span>]</span> <span>=</span> <span>[</span><span>2007.58</span><span>,</span> <span>1441.61</span><span>,</span> <span>1226.36</span><span>,</span> <span>875.47</span><span>,</span> <span>1368.88</span><span>,</span> <span>1328.24</span><span>,</span> <span>1177.12</span><span>,</span> <span>900.77</span><span>,</span> <span>910.68</span><span>,</span> <span>591.89</span><span>,</span> <span>610.55</span><span>,</span> <span>511.45</span><span>]</span>
<span>df</span><span>[</span><span>'tdp_watts'</span><span>]</span> <span>=</span> <span>[</span><span>450</span><span>,</span> <span>320</span><span>,</span> <span>285</span><span>,</span> <span>200</span><span>,</span> <span>450</span><span>,</span> <span>350</span><span>,</span> <span>350</span><span>,</span> <span>350</span><span>,</span> <span>290</span><span>,</span> <span>220</span><span>,</span> <span>200</span><span>,</span> <span>170</span><span>]</span>
<span>df</span><span>[</span><span>'ray_traycing_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>191</span><span>,</span> <span>112.7</span><span>,</span> <span>92.7</span><span>,</span> <span>None</span><span>,</span> <span>79.9</span><span>,</span> <span>71.1</span><span>,</span> <span>68.2</span><span>,</span> <span>61.3</span><span>,</span> <span>43.5</span><span>,</span> <span>40.6</span><span>,</span> <span>32.4</span><span>,</span> <span>25</span><span>]</span>
<span>df</span><span>[</span><span>'tensor_compute_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>292</span><span>,</span> <span>172</span><span>,</span> <span>142</span><span>,</span> <span>None</span><span>,</span> <span>269.1</span><span>,</span> <span>235.08</span><span>,</span> <span>228.6</span><span>,</span> <span>180.6</span><span>,</span> <span>154.8</span><span>,</span> <span>141.31</span><span>,</span> <span>109.7</span><span>,</span> <span>75.7</span><span>]</span>
<span>df</span><span>[</span><span>'half_precision_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>73.1</span><span>,</span> <span>43.0</span><span>,</span> <span>35.5</span><span>,</span> <span>22.6</span><span>,</span> <span>33.5</span><span>,</span> <span>29.38</span><span>,</span> <span>28.06</span><span>,</span> <span>22.6</span><span>,</span> <span>19.35</span><span>,</span> <span>17.66</span><span>,</span> <span>13.70</span><span>,</span> <span>9.46</span><span>]</span>
<span>df</span><span>[</span><span>'double_precision_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>1.142</span><span>,</span> <span>0.672</span><span>,</span> <span>0.554</span><span>,</span> <span>0.353</span><span>,</span> <span>0.524</span><span>,</span> <span>0.459</span><span>,</span> <span>0.438</span><span>,</span> <span>0.353</span><span>,</span> <span>0.302</span><span>,</span> <span>0.276</span><span>,</span> <span>0.214</span><span>,</span> <span>0.148</span><span>]</span>
<span>df</span><span>[</span><span>'single_precision_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>73.1</span><span>,</span> <span>43.0</span><span>,</span> <span>35.5</span><span>,</span> <span>22.6</span><span>,</span> <span>33.5</span><span>,</span> <span>29.28</span><span>,</span> <span>28.57</span><span>,</span> <span>22.6</span><span>,</span> <span>19.35</span><span>,</span> <span>17.66</span><span>,</span> <span>13.72</span><span>,</span> <span>9.46</span><span>]</span>
<span>df</span><span>[</span><span>'fillrate_gts'</span><span>]</span> <span>=</span> <span>[</span><span>1290.2</span><span>,</span> <span>761.5</span><span>,</span> <span>626.4</span><span>,</span> <span>455.4</span><span>,</span> <span>524.2</span><span>,</span> <span>457.6</span><span>,</span> <span>438.5</span><span>,</span> <span>352.8</span><span>,</span> <span>302.36</span><span>,</span> <span>276.0</span><span>,</span> <span>214.3</span><span>,</span> <span>147.8</span><span>]</span>
<span>df</span><span>[</span><span>'fillrate_gps'</span><span>]</span> <span>=</span> <span>[</span><span>443.5</span><span>,</span> <span>280.6</span><span>,</span> <span>208.8</span><span>,</span> <span>158.4</span><span>,</span> <span>174.7</span><span>,</span> <span>156.2</span><span>,</span> <span>153.5</span><span>,</span> <span>131.0</span><span>,</span> <span>151.18</span><span>,</span> <span>144.0</span><span>,</span> <span>112.8</span><span>,</span> <span>63.4</span><span>]</span>
<span>df</span><span>[</span><span>'clock_speed_mhz'</span><span>]</span> <span>=</span> <span>[</span><span>2230</span><span>,</span> <span>2210</span><span>,</span> <span>2310</span><span>,</span> <span>1920</span><span>,</span> <span>1560</span><span>,</span> <span>1395</span><span>,</span> <span>1365</span><span>,</span> <span>1260</span><span>,</span> <span>1575</span><span>,</span> <span>1500</span><span>,</span> <span>1410</span><span>,</span> <span>1320</span><span>]</span>
<span>for</span> <span>column</span> <span>in</span> <span>df</span><span>.</span><span>columns</span><span>[</span><span>3</span><span>:]:</span>
    <span>df</span><span>[</span><span>column</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'price'</span><span>]</span> <span>/</span> <span>df</span><span>[</span><span>column</span><span>]</span>
<span>df</span><span>[</span><span>'tdp_watts'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'tdp_watts'</span><span>]</span> <span>*</span> <span>-</span><span>1</span>
<span>df</span> <span>=</span> <span>df</span><span>.</span><span>sort_values</span><span>(</span><span>by</span><span>=</span><span>[</span><span>'release_date'</span><span>],</span> <span>ascending</span><span>=</span><span>True</span><span>)</span>
<span>df</span><span>[</span><span>'mean'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>df</span><span>.</span><span>columns</span><span>[</span><span>3</span><span>:]].</span><span>mean</span><span>(</span><span>axis</span><span>=</span><span>1</span><span>)</span>
<span>def</span> <span>pretty_panda</span><span>(</span><span>x</span><span>):</span>
    <span>return</span> <span>(</span><span>df</span><span>.</span><span>style</span>
                <span>.</span><span>background_gradient</span><span>()</span>
                <span>.</span><span>set_properties</span><span>(</span><span>**</span><span>{</span><span>'font-size'</span><span>:</span> <span>'10pt'</span><span>})</span>
                <span>.</span><span>format</span><span>(</span><span>precision</span><span>=</span><span>2</span><span>)</span>
                <span>.</span><span>set_caption</span><span>(</span><span>"NVIDIA GPUs Performace Comparison (value per 1$)"</span><span>)</span>
                <span>.</span><span>set_table_styles</span><span>([</span>
                    <span>{</span><span>'selector'</span><span>:</span> <span>'thead'</span><span>,</span> <span>'props'</span><span>:</span> <span>'text-transform: uppercase;'</span><span>},</span>
                    <span>{</span><span>'selector'</span><span>:</span> <span>'caption'</span><span>,</span> <span>'props'</span><span>:</span> <span>'font-size: 20px'</span><span>}</span>
                <span>]))</span>
<span>df</span> <span>=</span> <span>pretty_panda</span><span>(</span><span>df</span><span>)</span>
<span>df</span>
<span>import</span> <span>numpy</span> <span>as</span> <span>np</span>
<span>import</span> <span>matplotlib.pyplot</span> <span>as</span> <span>plt</span>
<span>import</span> <span>pandas</span> <span>as</span> <span>pd</span>
<span>import</span> <span>dataframe_image</span> <span>as</span> <span>dfi</span>

<span>plt</span><span>.</span><span>rcParams</span><span>[</span><span>"figure.dpi"</span><span>]</span> <span>=</span> <span>300</span>
<span>plt</span><span>.</span><span>rcParams</span><span>[</span><span>"savefig.dpi"</span><span>]</span> <span>=</span> <span>300</span>
<span>plt</span><span>.</span><span>rcParams</span><span>[</span><span>"figure.figsize"</span><span>]</span> <span>=</span> <span>[</span><span>12</span><span>,</span> <span>5</span><span>]</span>

<span>df</span> <span>=</span> <span>pd</span><span>.</span><span>DataFrame</span><span>()</span>
<span>df</span><span>[</span><span>'name'</span><span>]</span> <span>=</span> <span>[</span><span>'GeForce RTX 4090'</span><span>,</span> <span>'GeForce RTX 4080'</span><span>,</span> <span>'GeForce RTX 4070 Ti'</span><span>,</span> <span>'GeForce RTX 4070'</span><span>,</span> <span>'GeForce RTX 3090 Ti'</span><span>,</span> <span>'GeForce RTX 3090'</span><span>,</span> <span>'GeForce RTX 3080 Ti'</span><span>,</span> <span>'GeForce RTX 3080'</span><span>,</span> <span>'GeForce RTX 3070 Ti'</span><span>,</span> <span>'GeForce RTX 3070'</span><span>,</span> <span>'GeForce RTX 3060 Ti'</span><span>,</span> <span>'GeForce RTX 3060'</span><span>]</span>
<span>df</span><span>[</span><span>'release_date'</span><span>]</span> <span>=</span> <span>[</span><span>'October 12, 2022'</span><span>,</span> <span>'November 16, 2022'</span><span>,</span> <span>'January 5, 2023'</span><span>,</span> <span>'April 13, 2023'</span><span>,</span> <span>'March 29, 2022'</span><span>,</span> <span>'September 24, 2020'</span><span>,</span> <span>'June 3, 2021'</span><span>,</span> <span>'January 27, 2022'</span><span>,</span> <span>'June 10, 2021'</span><span>,</span> <span>'October 29, 2020'</span><span>,</span> <span>'October 27, 2022'</span><span>,</span> <span>'September 1, 2021'</span><span>]</span>
<span>df</span><span>[</span><span>'price'</span><span>]</span> <span>=</span> <span>[</span><span>2007.58</span><span>,</span> <span>1441.61</span><span>,</span> <span>1226.36</span><span>,</span> <span>875.47</span><span>,</span> <span>1368.88</span><span>,</span> <span>1328.24</span><span>,</span> <span>1177.12</span><span>,</span> <span>900.77</span><span>,</span> <span>910.68</span><span>,</span> <span>591.89</span><span>,</span> <span>610.55</span><span>,</span> <span>511.45</span><span>]</span>
<span>df</span><span>[</span><span>'tdp_watts'</span><span>]</span> <span>=</span> <span>[</span><span>450</span><span>,</span> <span>320</span><span>,</span> <span>285</span><span>,</span> <span>200</span><span>,</span> <span>450</span><span>,</span> <span>350</span><span>,</span> <span>350</span><span>,</span> <span>350</span><span>,</span> <span>290</span><span>,</span> <span>220</span><span>,</span> <span>200</span><span>,</span> <span>170</span><span>]</span>
<span>df</span><span>[</span><span>'ray_traycing_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>191</span><span>,</span> <span>112.7</span><span>,</span> <span>92.7</span><span>,</span> <span>None</span><span>,</span> <span>79.9</span><span>,</span> <span>71.1</span><span>,</span> <span>68.2</span><span>,</span> <span>61.3</span><span>,</span> <span>43.5</span><span>,</span> <span>40.6</span><span>,</span> <span>32.4</span><span>,</span> <span>25</span><span>]</span>
<span>df</span><span>[</span><span>'tensor_compute_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>292</span><span>,</span> <span>172</span><span>,</span> <span>142</span><span>,</span> <span>None</span><span>,</span> <span>269.1</span><span>,</span> <span>235.08</span><span>,</span> <span>228.6</span><span>,</span> <span>180.6</span><span>,</span> <span>154.8</span><span>,</span> <span>141.31</span><span>,</span> <span>109.7</span><span>,</span> <span>75.7</span><span>]</span>
<span>df</span><span>[</span><span>'half_precision_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>73.1</span><span>,</span> <span>43.0</span><span>,</span> <span>35.5</span><span>,</span> <span>22.6</span><span>,</span> <span>33.5</span><span>,</span> <span>29.38</span><span>,</span> <span>28.06</span><span>,</span> <span>22.6</span><span>,</span> <span>19.35</span><span>,</span> <span>17.66</span><span>,</span> <span>13.70</span><span>,</span> <span>9.46</span><span>]</span>
<span>df</span><span>[</span><span>'double_precision_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>1.142</span><span>,</span> <span>0.672</span><span>,</span> <span>0.554</span><span>,</span> <span>0.353</span><span>,</span> <span>0.524</span><span>,</span> <span>0.459</span><span>,</span> <span>0.438</span><span>,</span> <span>0.353</span><span>,</span> <span>0.302</span><span>,</span> <span>0.276</span><span>,</span> <span>0.214</span><span>,</span> <span>0.148</span><span>]</span>
<span>df</span><span>[</span><span>'single_precision_tflops'</span><span>]</span> <span>=</span> <span>[</span><span>73.1</span><span>,</span> <span>43.0</span><span>,</span> <span>35.5</span><span>,</span> <span>22.6</span><span>,</span> <span>33.5</span><span>,</span> <span>29.28</span><span>,</span> <span>28.57</span><span>,</span> <span>22.6</span><span>,</span> <span>19.35</span><span>,</span> <span>17.66</span><span>,</span> <span>13.72</span><span>,</span> <span>9.46</span><span>]</span>
<span>df</span><span>[</span><span>'fillrate_gts'</span><span>]</span> <span>=</span> <span>[</span><span>1290.2</span><span>,</span> <span>761.5</span><span>,</span> <span>626.4</span><span>,</span> <span>455.4</span><span>,</span> <span>524.2</span><span>,</span> <span>457.6</span><span>,</span> <span>438.5</span><span>,</span> <span>352.8</span><span>,</span> <span>302.36</span><span>,</span> <span>276.0</span><span>,</span> <span>214.3</span><span>,</span> <span>147.8</span><span>]</span>
<span>df</span><span>[</span><span>'fillrate_gps'</span><span>]</span> <span>=</span> <span>[</span><span>443.5</span><span>,</span> <span>280.6</span><span>,</span> <span>208.8</span><span>,</span> <span>158.4</span><span>,</span> <span>174.7</span><span>,</span> <span>156.2</span><span>,</span> <span>153.5</span><span>,</span> <span>131.0</span><span>,</span> <span>151.18</span><span>,</span> <span>144.0</span><span>,</span> <span>112.8</span><span>,</span> <span>63.4</span><span>]</span>
<span>df</span><span>[</span><span>'clock_speed_mhz'</span><span>]</span> <span>=</span> <span>[</span><span>2230</span><span>,</span> <span>2210</span><span>,</span> <span>2310</span><span>,</span> <span>1920</span><span>,</span> <span>1560</span><span>,</span> <span>1395</span><span>,</span> <span>1365</span><span>,</span> <span>1260</span><span>,</span> <span>1575</span><span>,</span> <span>1500</span><span>,</span> <span>1410</span><span>,</span> <span>1320</span><span>]</span>

<span>for</span> <span>column</span> <span>in</span> <span>df</span><span>.</span><span>columns</span><span>[</span><span>3</span><span>:]:</span>
    <span>df</span><span>[</span><span>column</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'price'</span><span>]</span> <span>/</span> <span>df</span><span>[</span><span>column</span><span>]</span>

<span>df</span><span>[</span><span>'tdp_watts'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>'tdp_watts'</span><span>]</span> <span>*</span> <span>-</span><span>1</span>
<span>df</span> <span>=</span> <span>df</span><span>.</span><span>sort_values</span><span>(</span><span>by</span><span>=</span><span>[</span><span>'release_date'</span><span>],</span> <span>ascending</span><span>=</span><span>True</span><span>)</span>
<span>df</span><span>[</span><span>'mean'</span><span>]</span> <span>=</span> <span>df</span><span>[</span><span>df</span><span>.</span><span>columns</span><span>[</span><span>3</span><span>:]].</span><span>mean</span><span>(</span><span>axis</span><span>=</span><span>1</span><span>)</span>

<span>def</span> <span>pretty_panda</span><span>(</span><span>x</span><span>):</span>
    <span>return</span> <span>(</span><span>df</span><span>.</span><span>style</span>
                <span>.</span><span>background_gradient</span><span>()</span>
                <span>.</span><span>set_properties</span><span>(</span><span>**</span><span>{</span><span>'font-size'</span><span>:</span> <span>'10pt'</span><span>})</span>
                <span>.</span><span>format</span><span>(</span><span>precision</span><span>=</span><span>2</span><span>)</span>
                <span>.</span><span>set_caption</span><span>(</span><span>"NVIDIA GPUs Performace Comparison (value per 1$)"</span><span>)</span>
                <span>.</span><span>set_table_styles</span><span>([</span>
                    <span>{</span><span>'selector'</span><span>:</span> <span>'thead'</span><span>,</span> <span>'props'</span><span>:</span> <span>'text-transform: uppercase;'</span><span>},</span>
                    <span>{</span><span>'selector'</span><span>:</span> <span>'caption'</span><span>,</span> <span>'props'</span><span>:</span> <span>'font-size: 20px'</span><span>}</span>
                <span>]))</span>

<span>df</span> <span>=</span> <span>pretty_panda</span><span>(</span><span>df</span><span>)</span>
<span>df</span>
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import dataframe_image as dfi

plt.rcParams["figure.dpi"] = 300
plt.rcParams["savefig.dpi"] = 300
plt.rcParams["figure.figsize"] = [12, 5]

df = pd.DataFrame()
df['name'] = ['GeForce RTX 4090', 'GeForce RTX 4080', 'GeForce RTX 4070 Ti', 'GeForce RTX 4070', 'GeForce RTX 3090 Ti', 'GeForce RTX 3090', 'GeForce RTX 3080 Ti', 'GeForce RTX 3080', 'GeForce RTX 3070 Ti', 'GeForce RTX 3070', 'GeForce RTX 3060 Ti', 'GeForce RTX 3060']
df['release_date'] = ['October 12, 2022', 'November 16, 2022', 'January 5, 2023', 'April 13, 2023', 'March 29, 2022', 'September 24, 2020', 'June 3, 2021', 'January 27, 2022', 'June 10, 2021', 'October 29, 2020', 'October 27, 2022', 'September 1, 2021']
df['price'] = [2007.58, 1441.61, 1226.36, 875.47, 1368.88, 1328.24, 1177.12, 900.77, 910.68, 591.89, 610.55, 511.45]
df['tdp_watts'] = [450, 320, 285, 200, 450, 350, 350, 350, 290, 220, 200, 170]
df['ray_traycing_tflops'] = [191, 112.7, 92.7, None, 79.9, 71.1, 68.2, 61.3, 43.5, 40.6, 32.4, 25]
df['tensor_compute_tflops'] = [292, 172, 142, None, 269.1, 235.08, 228.6, 180.6, 154.8, 141.31, 109.7, 75.7]
df['half_precision_tflops'] = [73.1, 43.0, 35.5, 22.6, 33.5, 29.38, 28.06, 22.6, 19.35, 17.66, 13.70, 9.46]
df['double_precision_tflops'] = [1.142, 0.672, 0.554, 0.353, 0.524, 0.459, 0.438, 0.353, 0.302, 0.276, 0.214, 0.148]
df['single_precision_tflops'] = [73.1, 43.0, 35.5, 22.6, 33.5, 29.28, 28.57, 22.6, 19.35, 17.66, 13.72, 9.46]
df['fillrate_gts'] = [1290.2, 761.5, 626.4, 455.4, 524.2, 457.6, 438.5, 352.8, 302.36, 276.0, 214.3, 147.8]
df['fillrate_gps'] = [443.5, 280.6, 208.8, 158.4, 174.7, 156.2, 153.5, 131.0, 151.18, 144.0, 112.8, 63.4]
df['clock_speed_mhz'] = [2230, 2210, 2310, 1920, 1560, 1395, 1365, 1260, 1575, 1500, 1410, 1320]

for column in df.columns[3:]:
    df[column] = df['price'] / df[column]

df['tdp_watts'] = df['tdp_watts'] * -1
df = df.sort_values(by=['release_date'], ascending=True)
df['mean'] = df[df.columns[3:]].mean(axis=1)

def pretty_panda(x):
    return (df.style
                .background_gradient()
                .set_properties(**{'font-size': '10pt'})
                .format(precision=2)
                .set_caption("NVIDIA GPUs Performace Comparison (value per 1$)")
                .set_table_styles([
                    {'selector': 'thead', 'props': 'text-transform: uppercase;'},
                    {'selector': 'caption', 'props': 'font-size: 20px'}
                ]))

df = pretty_panda(df)
df
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原文链接：NVIDIA GPUs Performace Comparison (value per 1$)
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