阿里云计算型实例云服务器收费标准及最新活动价格参考
计算型实例云服务器是很多用户在购买阿里云服务器时的首选云服务器实例规格，因为计算型实例云服务器的CPU与内存配比大多都是1：2，能够充分利用云服务器的cpu与内存资源，计算型实例云服务器通常适用于Web服务器、广告、游戏等企业通用业务场景。https://upload-images.jianshu.io/upload_images/19316870-d23ad6c86eee5f8c.png
<h2>阿里云服务器计算型实例规格有哪些？</h2>
目前属于计算型实例云服务器的实例规格有:
[*]计算型实例规格族c8a
[*]计算型实例规格族c8i
[*]计算平衡增强型实例规格族c8ae
[*]计算型实例规格族c8y
[*]RDMA增强型实例规格族c7re
[*]存储增强计算型实例规格族c7se
[*]网络增强计算型实例规格族c7nex
[*]计算型实例规格族c7a
[*]计算型实例规格族c7
[*]安全增强计算型实例规格族c7t
[*]计算型实例规格族c6r
[*]计算型实例规格族c6a
[*]安全增强计算型实例规格族c6t
[*]计算平衡增强型实例规格族c6e
[*]计算型实例规格族c6
[*]密集计算型实例规格族ic5
[*]计算型实例规格族c5
[*]计算网络增强型实例规格族sn1ne
<h2>阿里云计算型实例云服务器收费标准</h2>
下面是阿里云所有计算型实例云服务器的可选配置及按量(小时)收费标准、标准目录月价、优惠月价、年付月价、3年付月价和5年付月价，
<table>
<thead>
<tr>
<th>实例规格</th>
<th>vCPUs</th>
<th>内存(GiB)</th>
<th>按量(小时)</th>
<th>标准目录月价</th>
<th>优惠月价</th>
<th>年付月价</th>
<th>3年付月价</th>
<th>5年付月价</th>
</tr>
</thead>
<tbody>
<tr>
<td>ARM 计算型 ecs.c8y.small</td>
<td>1</td>
<td>2</td>
<td>0.133466</td>
<td>64.06</td>
<td>64.06</td>
<td>46.13</td>
<td>30.11</td>
<td>20.5</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c8y.large</td>
<td>2</td>
<td>4</td>
<td>0.266933</td>
<td>128.13</td>
<td>128.13</td>
<td>92.25</td>
<td>60.22</td>
<td>41</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c8y.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.533866</td>
<td>256.26</td>
<td>256.26</td>
<td>184.5</td>
<td>120.44</td>
<td>82</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c8y.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.067733</td>
<td>512.51</td>
<td>512.51</td>
<td>369.01</td>
<td>240.88</td>
<td>164</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c8y.4xlarge</td>
<td>16</td>
<td>32</td>
<td>2.135466</td>
<td>1025.02</td>
<td>1025.02</td>
<td>738.02</td>
<td>481.76</td>
<td>328.01</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c8y.8xlarge</td>
<td>32</td>
<td>64</td>
<td>4.270933</td>
<td>2050.05</td>
<td>2050.05</td>
<td>1476.03</td>
<td>963.52</td>
<td>656.02</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c8y.16xlarge</td>
<td>64</td>
<td>128</td>
<td>8.541866</td>
<td>4100.1</td>
<td>4100.1</td>
<td>2952.07</td>
<td>1927.05</td>
<td>1312.03</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.large</td>
<td>2</td>
<td>4</td>
<td>0.3872</td>
<td>185.86</td>
<td>185.86</td>
<td>157.98</td>
<td>102.22</td>
<td>70.63</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.7744</td>
<td>371.71</td>
<td>371.71</td>
<td>315.95</td>
<td>204.44</td>
<td>141.25</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.5488</td>
<td>743.42</td>
<td>743.42</td>
<td>631.91</td>
<td>408.88</td>
<td>282.5</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.0976</td>
<td>1486.85</td>
<td>1486.85</td>
<td>1263.82</td>
<td>817.77</td>
<td>565</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.8xlarge</td>
<td>32</td>
<td>64</td>
<td>6.1952</td>
<td>2973.7</td>
<td>2973.7</td>
<td>2527.64</td>
<td>1635.53</td>
<td>1130</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.12xlarge</td>
<td>48</td>
<td>96</td>
<td>9.2928</td>
<td>4460.54</td>
<td>4460.54</td>
<td>3791.46</td>
<td>2453.3</td>
<td>1695.01</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.16xlarge</td>
<td>64</td>
<td>128</td>
<td>12.3904</td>
<td>5947.39</td>
<td>5947.39</td>
<td>5055.28</td>
<td>3271.07</td>
<td>2260.01</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.24xlarge</td>
<td>96</td>
<td>192</td>
<td>18.5856</td>
<td>8921.09</td>
<td>8921.09</td>
<td>7582.93</td>
<td>4906.6</td>
<td>3390.01</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.32xlarge</td>
<td>128</td>
<td>256</td>
<td>24.7808</td>
<td>11894.78</td>
<td>11894.78</td>
<td>10110.57</td>
<td>6542.13</td>
<td>4520.02</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c8a.48xlarge</td>
<td>192</td>
<td>384</td>
<td>37.1712</td>
<td>17842.18</td>
<td>17842.18</td>
<td>15165.85</td>
<td>9813.2</td>
<td>6780.03</td>
</tr>
<tr>
<td>计算型 ecs.c8i.large</td>
<td>2</td>
<td>4</td>
<td>0.4281</td>
<td>205.48</td>
<td>205.48</td>
<td>174.66</td>
<td>113.01</td>
<td>78.08</td>
</tr>
<tr>
<td>计算型 ecs.c8i.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.8562</td>
<td>410.96</td>
<td>410.96</td>
<td>349.32</td>
<td>226.03</td>
<td>156.17</td>
</tr>
<tr>
<td>计算型 ecs.c8i.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.7123</td>
<td>821.92</td>
<td>821.92</td>
<td>698.63</td>
<td>452.06</td>
<td>312.33</td>
</tr>
<tr>
<td>计算型 ecs.c8i.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.5685</td>
<td>1232.88</td>
<td>1232.88</td>
<td>1047.95</td>
<td>678.09</td>
<td>468.5</td>
</tr>
<tr>
<td>计算型 ecs.c8i.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.4247</td>
<td>1643.84</td>
<td>1643.84</td>
<td>1397.27</td>
<td>904.11</td>
<td>624.66</td>
</tr>
<tr>
<td>计算型 ecs.c8i.6xlarge</td>
<td>24</td>
<td>48</td>
<td>5.137</td>
<td>2465.76</td>
<td>2465.76</td>
<td>2095.9</td>
<td>1356.17</td>
<td>936.99</td>
</tr>
<tr>
<td>计算型 ecs.c8i.8xlarge</td>
<td>32</td>
<td>64</td>
<td>6.8493</td>
<td>3287.68</td>
<td>3287.68</td>
<td>2794.53</td>
<td>1808.23</td>
<td>1249.32</td>
</tr>
<tr>
<td>计算型 ecs.c8i.12xlarge</td>
<td>48</td>
<td>96</td>
<td>10.274</td>
<td>4931.53</td>
<td>4931.53</td>
<td>4191.8</td>
<td>2712.34</td>
<td>1873.98</td>
</tr>
<tr>
<td>计算型 ecs.c8i.16xlarge</td>
<td>64</td>
<td>128</td>
<td>13.6987</td>
<td>6575.37</td>
<td>6575.37</td>
<td>5589.06</td>
<td>3616.45</td>
<td>2498.64</td>
</tr>
<tr>
<td>计算型 ecs.c8i.24xlarge</td>
<td>96</td>
<td>192</td>
<td>20.548</td>
<td>9863.05</td>
<td>9863.05</td>
<td>8383.59</td>
<td>5424.68</td>
<td>3747.96</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.large</td>
<td>2</td>
<td>4</td>
<td>0.470833</td>
<td>226</td>
<td>226</td>
<td>192.1</td>
<td>124.3</td>
<td>85.88</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.941666</td>
<td>452</td>
<td>452</td>
<td>384.2</td>
<td>248.6</td>
<td>171.76</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.883333</td>
<td>904</td>
<td>904</td>
<td>768.4</td>
<td>497.2</td>
<td>343.52</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.825</td>
<td>1356</td>
<td>1356</td>
<td>1152.6</td>
<td>745.8</td>
<td>515.28</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.766666</td>
<td>1808</td>
<td>1808</td>
<td>1536.8</td>
<td>994.4</td>
<td>687.04</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.6xlarge</td>
<td>24</td>
<td>48</td>
<td>5.65</td>
<td>2712</td>
<td>2712</td>
<td>2305.2</td>
<td>1491.6</td>
<td>1030.56</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.8xlarge</td>
<td>32</td>
<td>64</td>
<td>7.533333</td>
<td>3616</td>
<td>3616</td>
<td>3073.6</td>
<td>1988.8</td>
<td>1374.08</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.12xlarge</td>
<td>48</td>
<td>96</td>
<td>11.3</td>
<td>5424</td>
<td>5424</td>
<td>4610.4</td>
<td>2983.2</td>
<td>2061.12</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc7.24xlarge</td>
<td>96</td>
<td>192</td>
<td>22.6</td>
<td>10848</td>
<td>10848</td>
<td>9220.8</td>
<td>5966.4</td>
<td>4122.24</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn7.26xlarge</td>
<td>104</td>
<td>768</td>
<td>252.666666</td>
<td>121280</td>
<td>121280</td>
<td>103088</td>
<td>66704</td>
<td>46086.4</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7-c12g1.3xlarge</td>
<td>12</td>
<td>94</td>
<td>31.583333</td>
<td>15160</td>
<td>15160</td>
<td>12886</td>
<td>8338</td>
<td>5760.8</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7-c13g1.13xlarge</td>
<td>52</td>
<td>378</td>
<td>126.333333</td>
<td>60640</td>
<td>60640</td>
<td>51544</td>
<td>33352</td>
<td>23043.2</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7-c13g1.26xlarge</td>
<td>104</td>
<td>756</td>
<td>252.666666</td>
<td>121280</td>
<td>121280</td>
<td>103088</td>
<td>66704</td>
<td>46086.4</td>
</tr>
<tr>
<td>计算型 ecs.c7.large</td>
<td>2</td>
<td>4</td>
<td>0.407698</td>
<td>195.7</td>
<td>195.7</td>
<td>140.9</td>
<td>91.98</td>
<td>62.62</td>
</tr>
<tr>
<td>计算型 ecs.c7.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.815397</td>
<td>391.39</td>
<td>391.39</td>
<td>281.8</td>
<td>183.95</td>
<td>125.25</td>
</tr>
<tr>
<td>计算型 ecs.c7.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.630795</td>
<td>782.78</td>
<td>782.78</td>
<td>563.6</td>
<td>367.91</td>
<td>250.49</td>
</tr>
<tr>
<td>计算型 ecs.c7.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.446193</td>
<td>1174.17</td>
<td>1174.17</td>
<td>845.41</td>
<td>551.86</td>
<td>375.74</td>
</tr>
<tr>
<td>计算型 ecs.c7.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.261591</td>
<td>1565.56</td>
<td>1565.56</td>
<td>1127.21</td>
<td>735.82</td>
<td>500.98</td>
</tr>
<tr>
<td>计算型 ecs.c7.6xlarge</td>
<td>24</td>
<td>48</td>
<td>4.892387</td>
<td>2348.35</td>
<td>2348.35</td>
<td>1690.81</td>
<td>1103.72</td>
<td>751.47</td>
</tr>
<tr>
<td>计算型 ecs.c7.8xlarge</td>
<td>32</td>
<td>64</td>
<td>6.523183</td>
<td>3131.13</td>
<td>3131.13</td>
<td>2254.41</td>
<td>1471.63</td>
<td>1001.96</td>
</tr>
<tr>
<td>计算型 ecs.c7.16xlarge</td>
<td>64</td>
<td>128</td>
<td>13.046366</td>
<td>6262.26</td>
<td>6262.26</td>
<td>4508.82</td>
<td>2943.26</td>
<td>2003.92</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc7.32xlarge</td>
<td>128</td>
<td>256</td>
<td>26.092733</td>
<td>12524.51</td>
<td>12524.51</td>
<td>10645.84</td>
<td>6888.48</td>
<td>4759.31</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.large</td>
<td>2</td>
<td>4</td>
<td>0.470421</td>
<td>225.8</td>
<td>225.8</td>
<td>191.93</td>
<td>124.19</td>
<td>85.81</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.940843</td>
<td>451.61</td>
<td>451.61</td>
<td>383.86</td>
<td>248.38</td>
<td>171.61</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.881687</td>
<td>903.21</td>
<td>903.21</td>
<td>767.73</td>
<td>496.77</td>
<td>343.22</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.822531</td>
<td>1354.82</td>
<td>1354.82</td>
<td>1151.59</td>
<td>745.15</td>
<td>514.83</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.763375</td>
<td>1806.42</td>
<td>1806.42</td>
<td>1535.46</td>
<td>993.53</td>
<td>686.44</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.6xlarge</td>
<td>24</td>
<td>48</td>
<td>5.645062</td>
<td>2709.63</td>
<td>2709.63</td>
<td>2303.19</td>
<td>1490.3</td>
<td>1029.66</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.8xlarge</td>
<td>32</td>
<td>64</td>
<td>7.52675</td>
<td>3612.84</td>
<td>3612.84</td>
<td>3070.91</td>
<td>1987.06</td>
<td>1372.88</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c7t.16xlarge</td>
<td>64</td>
<td>128</td>
<td>15.0535</td>
<td>7225.68</td>
<td>7225.68</td>
<td>6141.83</td>
<td>3974.12</td>
<td>2745.76</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn7e.32xlarge</td>
<td>128</td>
<td>1024</td>
<td>277.933</td>
<td>133408</td>
<td>133408</td>
<td>113396.8</td>
<td>73374.4</td>
<td>50695.04</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn7i.32xlarge</td>
<td>128</td>
<td>768</td>
<td>53.2331</td>
<td>25551.9</td>
<td>25551.9</td>
<td>21719.12</td>
<td>14053.55</td>
<td>9709.72</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc7a.64xlarge</td>
<td>256</td>
<td>512</td>
<td>37.547</td>
<td>18022.4</td>
<td>18022.4</td>
<td>15319.04</td>
<td>9912.32</td>
<td>6848.51</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a.large</td>
<td>2</td>
<td>4</td>
<td>0.293</td>
<td>140.8</td>
<td>140.8</td>
<td>119.68</td>
<td>77.44</td>
<td>53.5</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.587</td>
<td>281.6</td>
<td>281.6</td>
<td>239.36</td>
<td>154.88</td>
<td>107.01</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.173</td>
<td>563.2</td>
<td>563.2</td>
<td>478.72</td>
<td>309.76</td>
<td>214.02</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a.4xlarge</td>
<td>16</td>
<td>32</td>
<td>2.347</td>
<td>1126.4</td>
<td>1126.4</td>
<td>957.44</td>
<td>619.52</td>
<td>428.03</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a.8xlarge</td>
<td>32</td>
<td>64</td>
<td>4.693</td>
<td>2252.8</td>
<td>2252.8</td>
<td>1914.88</td>
<td>1239.04</td>
<td>856.06</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a-nps1.8xlarge</td>
<td>32</td>
<td>64</td>
<td>4.693</td>
<td>2252.8</td>
<td>2252.8</td>
<td>1914.88</td>
<td>1239.04</td>
<td>856.06</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a.16xlarge</td>
<td>64</td>
<td>128</td>
<td>9.387</td>
<td>4505.6</td>
<td>4505.6</td>
<td>3829.76</td>
<td>2478.08</td>
<td>1712.13</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a-nps1.16xlarge</td>
<td>64</td>
<td>128</td>
<td>9.387</td>
<td>4505.6</td>
<td>4505.6</td>
<td>3829.76</td>
<td>2478.08</td>
<td>1712.13</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c7a.32xlarge</td>
<td>128</td>
<td>256</td>
<td>18.773</td>
<td>9011.2</td>
<td>9011.2</td>
<td>7659.52</td>
<td>4956.16</td>
<td>3424.26</td>
</tr>
<tr>
<td>计算型超级计算集群 ecs.sccc7.32xlarge</td>
<td>128</td>
<td>256</td>
<td>43.01</td>
<td>20644.8</td>
<td>20644.8</td>
<td>17548.08</td>
<td>11354.64</td>
<td>7845.02</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7i-c8g1.2xlarge</td>
<td>8</td>
<td>30</td>
<td>9.5326</td>
<td>4575.66</td>
<td>4575.66</td>
<td>3889.31</td>
<td>2516.61</td>
<td>1738.75</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7i-c16g1.4xlarge</td>
<td>16</td>
<td>60</td>
<td>10.0934</td>
<td>4844.81</td>
<td>4844.81</td>
<td>4118.09</td>
<td>2664.65</td>
<td>1841.03</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7i-c32g1.8xlarge</td>
<td>32</td>
<td>188</td>
<td>13.3083</td>
<td>6387.98</td>
<td>6387.98</td>
<td>5429.78</td>
<td>3513.39</td>
<td>2427.43</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7i-c48g1.12xlarge</td>
<td>48</td>
<td>310</td>
<td>17.944</td>
<td>8613</td>
<td>8613</td>
<td>7321.05</td>
<td>4737.15</td>
<td>3272.94</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7i-c56g1.14xlarge</td>
<td>56</td>
<td>346</td>
<td>21.533</td>
<td>10335.6</td>
<td>10335.6</td>
<td>8785.26</td>
<td>5684.58</td>
<td>3927.53</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7i-c32g1.16xlarge</td>
<td>64</td>
<td>376</td>
<td>26.6166</td>
<td>12775.95</td>
<td>12775.95</td>
<td>10859.56</td>
<td>7026.77</td>
<td>4854.86</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7i-c32g1.32xlarge</td>
<td>128</td>
<td>752</td>
<td>53.2331</td>
<td>25551.9</td>
<td>25551.9</td>
<td>21719.12</td>
<td>14053.55</td>
<td>9709.72</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.large</td>
<td>2</td>
<td>4</td>
<td>0.515223</td>
<td>247.31</td>
<td>247.31</td>
<td>210.21</td>
<td>136.02</td>
<td>93.98</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.xlarge</td>
<td>4</td>
<td>8</td>
<td>1.030447</td>
<td>494.62</td>
<td>494.62</td>
<td>420.42</td>
<td>272.04</td>
<td>187.95</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.2xlarge</td>
<td>8</td>
<td>16</td>
<td>2.060895</td>
<td>989.23</td>
<td>989.23</td>
<td>840.85</td>
<td>544.08</td>
<td>375.91</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.3xlarge</td>
<td>12</td>
<td>24</td>
<td>3.091343</td>
<td>1483.85</td>
<td>1483.85</td>
<td>1261.27</td>
<td>816.11</td>
<td>563.86</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.4xlarge</td>
<td>16</td>
<td>32</td>
<td>4.121791</td>
<td>1978.46</td>
<td>1978.46</td>
<td>1681.69</td>
<td>1088.15</td>
<td>751.81</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.6xlarge</td>
<td>24</td>
<td>48</td>
<td>6.182687</td>
<td>2967.69</td>
<td>2967.69</td>
<td>2522.54</td>
<td>1632.23</td>
<td>1127.72</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.8xlarge</td>
<td>32</td>
<td>64</td>
<td>8.243583</td>
<td>3956.92</td>
<td>3956.92</td>
<td>3363.38</td>
<td>2176.31</td>
<td>1503.63</td>
</tr>
<tr>
<td>存储增强计算型 ecs.c7se.16xlarge</td>
<td>64</td>
<td>128</td>
<td>16.487166</td>
<td>7913.84</td>
<td>7913.84</td>
<td>6726.76</td>
<td>4352.61</td>
<td>3007.26</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7e-c16g1.4xlarge</td>
<td>16</td>
<td>125</td>
<td>34.742</td>
<td>16676</td>
<td>16676</td>
<td>14174.6</td>
<td>9171.8</td>
<td>6336.88</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7e-c16g1.16xlarge</td>
<td>64</td>
<td>500</td>
<td>138.967</td>
<td>66704</td>
<td>66704</td>
<td>56698.4</td>
<td>36687.2</td>
<td>25347.52</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn7e-c16g1.32xlarge</td>
<td>128</td>
<td>1000</td>
<td>277.933</td>
<td>133408</td>
<td>133408</td>
<td>113396.8</td>
<td>73374.4</td>
<td>50695.04</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn7ex.32xlarge</td>
<td>128</td>
<td>1024</td>
<td>270.8333</td>
<td>130000</td>
<td>130000</td>
<td>130000</td>
<td>130000</td>
<td>130000</td>
</tr>
<tr>
<td>ARM GPU计算型 ecs.gn7r-c16g1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>4.8667</td>
<td>2336</td>
<td>2336</td>
<td>2336</td>
<td>2336</td>
<td>2336</td>
</tr>
<tr>
<td>ARM GPU计算型 ecs.gn7r-c16g1.32xlarge</td>
<td>128</td>
<td>512</td>
<td>38.9333</td>
<td>18688</td>
<td>18688</td>
<td>18688</td>
<td>18688</td>
<td>18688</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6v-c8g1.2xlarge</td>
<td>8</td>
<td>32</td>
<td>26.46</td>
<td>7620</td>
<td>7620</td>
<td>3429</td>
<td>2209.8</td>
<td>2209.8</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6v-c8g1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>105.84</td>
<td>30480</td>
<td>30480</td>
<td>13716</td>
<td>8839.2</td>
<td>8839.2</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6v-c8g1.16xlarge</td>
<td>64</td>
<td>256</td>
<td>211.68</td>
<td>60960</td>
<td>60960</td>
<td>27432</td>
<td>17678.4</td>
<td>17678.4</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6v-c10g1.20xlarge</td>
<td>82</td>
<td>336</td>
<td>219.64</td>
<td>63255</td>
<td>63255</td>
<td>28464.75</td>
<td>18343.95</td>
<td>18343.95</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c4g1.xlarge</td>
<td>4</td>
<td>15</td>
<td>11.63</td>
<td>3348</td>
<td>3348</td>
<td>1674</td>
<td>1071.36</td>
<td>1071.36</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c8g1.2xlarge</td>
<td>8</td>
<td>31</td>
<td>14</td>
<td>4032</td>
<td>4032</td>
<td>2016</td>
<td>1290.24</td>
<td>1290.24</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c16g1.4xlarge</td>
<td>16</td>
<td>62</td>
<td>16.41</td>
<td>4725</td>
<td>4725</td>
<td>2362.5</td>
<td>1512</td>
<td>1512</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c24g1.6xlarge</td>
<td>24</td>
<td>93</td>
<td>17.19</td>
<td>4950</td>
<td>4950</td>
<td>2475</td>
<td>1584</td>
<td>1584</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c40g1.10xlarge</td>
<td>40</td>
<td>155</td>
<td>14.819</td>
<td>7112.9</td>
<td>7112.9</td>
<td>3556.45</td>
<td>2276.13</td>
<td>2276.13</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c24g1.12xlarge</td>
<td>48</td>
<td>186</td>
<td>34.38</td>
<td>9900</td>
<td>9900</td>
<td>4950</td>
<td>3168</td>
<td>3168</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6i-c24g1.24xlarge</td>
<td>96</td>
<td>372</td>
<td>68.75</td>
<td>19800</td>
<td>19800</td>
<td>9900</td>
<td>6336</td>
<td>6336</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn6i.24xlarge</td>
<td>96</td>
<td>384</td>
<td>68.75</td>
<td>19800</td>
<td>19800</td>
<td>16830</td>
<td>10890</td>
<td>7524</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc6e.26xlarge</td>
<td>104</td>
<td>192</td>
<td>21.342</td>
<td>10244</td>
<td>10244</td>
<td>8707.4</td>
<td>5634.2</td>
<td>3892.72</td>
</tr>
<tr>
<td>计算型 ecs.c6.large</td>
<td>2</td>
<td>4</td>
<td>0.39</td>
<td>187</td>
<td>187</td>
<td>158.95</td>
<td>102.85</td>
<td>71.06</td>
</tr>
<tr>
<td>计算型 ecs.c6.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.78</td>
<td>374</td>
<td>374</td>
<td>317.9</td>
<td>205.7</td>
<td>142.12</td>
</tr>
<tr>
<td>计算型 ecs.c6.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.56</td>
<td>748</td>
<td>748</td>
<td>635.8</td>
<td>411.4</td>
<td>284.24</td>
</tr>
<tr>
<td>计算型 ecs.c6.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.34</td>
<td>1122</td>
<td>1122</td>
<td>953.7</td>
<td>617.1</td>
<td>426.36</td>
</tr>
<tr>
<td>计算型 ecs.c6.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.12</td>
<td>1496</td>
<td>1496</td>
<td>1271.6</td>
<td>822.8</td>
<td>568.48</td>
</tr>
<tr>
<td>计算型 ecs.c6.6xlarge</td>
<td>24</td>
<td>48</td>
<td>4.68</td>
<td>2244</td>
<td>2244</td>
<td>1907.4</td>
<td>1234.2</td>
<td>852.72</td>
</tr>
<tr>
<td>计算型 ecs.c6.8xlarge</td>
<td>32</td>
<td>64</td>
<td>6.23</td>
<td>2992</td>
<td>2992</td>
<td>2543.2</td>
<td>1645.6</td>
<td>1136.96</td>
</tr>
<tr>
<td>计算型 ecs.c6.13xlarge</td>
<td>52</td>
<td>96</td>
<td>10.13</td>
<td>4862</td>
<td>4862</td>
<td>4132.7</td>
<td>2674.1</td>
<td>1847.56</td>
</tr>
<tr>
<td>计算型 ecs.c6.26xlarge</td>
<td>104</td>
<td>192</td>
<td>20.26</td>
<td>9724</td>
<td>9724</td>
<td>8265.4</td>
<td>5348.2</td>
<td>3695.12</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn6e.24xlarge</td>
<td>96</td>
<td>768</td>
<td>157.92</td>
<td>75800</td>
<td>75800</td>
<td>75800</td>
<td>75800</td>
<td>75800</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.large</td>
<td>2</td>
<td>4</td>
<td>0.447916</td>
<td>215</td>
<td>215</td>
<td>182.75</td>
<td>118.25</td>
<td>81.7</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.895833</td>
<td>430</td>
<td>430</td>
<td>365.5</td>
<td>236.5</td>
<td>163.4</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.791666</td>
<td>860</td>
<td>860</td>
<td>731</td>
<td>473</td>
<td>326.8</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.3xlarge</td>
<td>12</td>
<td>24</td>
<td>2.6875</td>
<td>1290</td>
<td>1290</td>
<td>1096.5</td>
<td>709.5</td>
<td>490.2</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.583333</td>
<td>1720</td>
<td>1720</td>
<td>1462</td>
<td>946</td>
<td>653.6</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.6xlarge</td>
<td>24</td>
<td>48</td>
<td>5.375</td>
<td>2580</td>
<td>2580</td>
<td>2193</td>
<td>1419</td>
<td>980.4</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.8xlarge</td>
<td>32</td>
<td>64</td>
<td>7.166666</td>
<td>3440</td>
<td>3440</td>
<td>2924</td>
<td>1892</td>
<td>1307.2</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.10xlarge</td>
<td>40</td>
<td>96</td>
<td>8.958333</td>
<td>4300</td>
<td>4300</td>
<td>3655</td>
<td>2365</td>
<td>1634</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.16xlarge</td>
<td>64</td>
<td>128</td>
<td>14.333333</td>
<td>6880</td>
<td>6880</td>
<td>5848</td>
<td>3784</td>
<td>2614.4</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc6.20xlarge</td>
<td>80</td>
<td>192</td>
<td>17.916666</td>
<td>8600</td>
<td>8600</td>
<td>7310</td>
<td>4730</td>
<td>3268</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6e-c12g1.3xlarge</td>
<td>12</td>
<td>92</td>
<td>19.739</td>
<td>9475</td>
<td>9475</td>
<td>9475</td>
<td>9475</td>
<td>9475</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6e-c12g1.12xlarge</td>
<td>48</td>
<td>368</td>
<td>78.958</td>
<td>37900</td>
<td>37900</td>
<td>37900</td>
<td>37900</td>
<td>37900</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn6e-c12g1.24xlarge</td>
<td>96</td>
<td>736</td>
<td>157.916</td>
<td>75800</td>
<td>75800</td>
<td>75800</td>
<td>75800</td>
<td>75800</td>
</tr>
<tr>
<td>GPU计算型弹性裸金属服务器 ecs.ebmgn6v.24xlarge</td>
<td>96</td>
<td>384</td>
<td>237.125</td>
<td>68292</td>
<td>68292</td>
<td>68292</td>
<td>68292</td>
<td>68292</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc6.26xlarge</td>
<td>104</td>
<td>192</td>
<td>20.26</td>
<td>9724</td>
<td>9724</td>
<td>8265.4</td>
<td>5348.2</td>
<td>3695.12</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc6a.64xlarge</td>
<td>256</td>
<td>512</td>
<td>37.547</td>
<td>18022.4</td>
<td>18022.4</td>
<td>18022.4</td>
<td>18022.4</td>
<td>18022.4</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c6t.large</td>
<td>2</td>
<td>4</td>
<td>0.422916</td>
<td>203</td>
<td>203</td>
<td>172.55</td>
<td>111.65</td>
<td>77.14</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c6t.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.845833</td>
<td>406</td>
<td>406</td>
<td>345.1</td>
<td>223.3</td>
<td>154.28</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c6t.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.691666</td>
<td>812</td>
<td>812</td>
<td>690.2</td>
<td>446.6</td>
<td>308.56</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c6t.4xlarge</td>
<td>16</td>
<td>32</td>
<td>3.383333</td>
<td>1624</td>
<td>1624</td>
<td>1380.4</td>
<td>893.2</td>
<td>617.12</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c6t.8xlarge</td>
<td>32</td>
<td>64</td>
<td>6.764583</td>
<td>3247</td>
<td>3247</td>
<td>2759.95</td>
<td>1785.85</td>
<td>1233.86</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c6t.13xlarge</td>
<td>52</td>
<td>96</td>
<td>10.991666</td>
<td>5276</td>
<td>5276</td>
<td>4484.6</td>
<td>2901.8</td>
<td>2004.88</td>
</tr>
<tr>
<td>安全增强计算型 ecs.c6t.26xlarge</td>
<td>104</td>
<td>192</td>
<td>21.983333</td>
<td>10552</td>
<td>10552</td>
<td>8969.2</td>
<td>5803.6</td>
<td>4009.76</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c6a.large</td>
<td>2</td>
<td>4</td>
<td>0.293</td>
<td>140.8</td>
<td>140.8</td>
<td>119.68</td>
<td>77.44</td>
<td>53.5</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c6a.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.587</td>
<td>281.6</td>
<td>281.6</td>
<td>239.36</td>
<td>154.88</td>
<td>107.01</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c6a.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.173</td>
<td>563.2</td>
<td>563.2</td>
<td>478.72</td>
<td>309.76</td>
<td>214.02</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c6a.4xlarge</td>
<td>16</td>
<td>32</td>
<td>2.347</td>
<td>1126.4</td>
<td>1126.4</td>
<td>957.44</td>
<td>619.52</td>
<td>428.03</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c6a.8xlarge</td>
<td>32</td>
<td>64</td>
<td>4.693</td>
<td>2252.8</td>
<td>2252.8</td>
<td>1914.88</td>
<td>1239.04</td>
<td>856.06</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c6a.16xlarge</td>
<td>64</td>
<td>128</td>
<td>9.387</td>
<td>4505.6</td>
<td>4505.6</td>
<td>3829.76</td>
<td>2478.08</td>
<td>1712.13</td>
</tr>
<tr>
<td>AMD 计算型 ecs.c6a.32xlarge</td>
<td>128</td>
<td>256</td>
<td>18.773</td>
<td>9011.2</td>
<td>9011.2</td>
<td>7659.52</td>
<td>4956.16</td>
<td>3424.26</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c6r.large</td>
<td>2</td>
<td>4</td>
<td>0.311666</td>
<td>149.6</td>
<td>149.6</td>
<td>127.16</td>
<td>82.28</td>
<td>56.85</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c6r.xlarge</td>
<td>4</td>
<td>8</td>
<td>0.623333</td>
<td>299.2</td>
<td>299.2</td>
<td>254.32</td>
<td>164.56</td>
<td>113.7</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c6r.2xlarge</td>
<td>8</td>
<td>16</td>
<td>1.246666</td>
<td>598.4</td>
<td>598.4</td>
<td>508.64</td>
<td>329.12</td>
<td>227.39</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c6r.4xlarge</td>
<td>16</td>
<td>32</td>
<td>2.493333</td>
<td>1196.8</td>
<td>1196.8</td>
<td>1017.28</td>
<td>658.24</td>
<td>454.78</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c6r.8xlarge</td>
<td>32</td>
<td>64</td>
<td>4.986666</td>
<td>2393.6</td>
<td>2393.6</td>
<td>2034.56</td>
<td>1316.48</td>
<td>909.57</td>
</tr>
<tr>
<td>ARM 计算型 ecs.c6r.16xlarge</td>
<td>64</td>
<td>128</td>
<td>9.973333</td>
<td>4787.2</td>
<td>4787.2</td>
<td>4069.12</td>
<td>2632.96</td>
<td>1819.14</td>
</tr>
<tr>
<td>ARM GPU计算型弹性裸金属服务器 ecs.ebmgn6ia.20xlarge</td>
<td>80</td>
<td>256</td>
<td>33.006185</td>
<td>15842.97</td>
<td>15842.97</td>
<td>13466.52</td>
<td>8713.63</td>
<td>6020.33</td>
</tr>
<tr>
<td>GPU图形计算型 ecs.ebmgi6s.24xlarge</td>
<td>96</td>
<td>384</td>
<td>15.09375</td>
<td>7245</td>
<td>7245</td>
<td>7245</td>
<td>7245</td>
<td>7245</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c4g1.xlarge</td>
<td>4</td>
<td>30</td>
<td>12.78</td>
<td>3681</td>
<td>3681</td>
<td>3128.85</td>
<td>1914.12</td>
<td>1288.35</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.2xlarge</td>
<td>8</td>
<td>60</td>
<td>15.39</td>
<td>4433</td>
<td>4433</td>
<td>3768.05</td>
<td>2305.16</td>
<td>1551.55</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c4g1.2xlarge</td>
<td>8</td>
<td>60</td>
<td>25.57</td>
<td>7363</td>
<td>7363</td>
<td>6258.55</td>
<td>3828.76</td>
<td>2577.05</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.4xlarge</td>
<td>16</td>
<td>120</td>
<td>30.78</td>
<td>8866</td>
<td>8866</td>
<td>7536.1</td>
<td>4610.32</td>
<td>3103.1</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c28g1.7xlarge</td>
<td>28</td>
<td>112</td>
<td>23.88</td>
<td>6877</td>
<td>6877</td>
<td>5845.45</td>
<td>3576.04</td>
<td>2406.95</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.8xlarge</td>
<td>32</td>
<td>240</td>
<td>61.57</td>
<td>17731</td>
<td>17731</td>
<td>15071.35</td>
<td>9220.12</td>
<td>6205.85</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c8g1.14xlarge</td>
<td>54</td>
<td>480</td>
<td>123.13</td>
<td>35462</td>
<td>35462</td>
<td>30142.7</td>
<td>18440.24</td>
<td>12411.7</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5-c28g1.14xlarge</td>
<td>56</td>
<td>224</td>
<td>47.75</td>
<td>13753</td>
<td>13753</td>
<td>11690.05</td>
<td>7151.56</td>
<td>4813.55</td>
</tr>
<tr>
<td>计算型 ecs.c5.large</td>
<td>2</td>
<td>4</td>
<td>0.62</td>
<td>179</td>
<td>179</td>
<td>152.15</td>
<td>98.45</td>
<td>68.02</td>
</tr>
<tr>
<td>计算型 ecs.c5.xlarge</td>
<td>4</td>
<td>8</td>
<td>1.24</td>
<td>358</td>
<td>358</td>
<td>304.3</td>
<td>196.9</td>
<td>136.04</td>
</tr>
<tr>
<td>计算型 ecs.c5.2xlarge</td>
<td>8</td>
<td>16</td>
<td>2.49</td>
<td>716</td>
<td>716</td>
<td>608.6</td>
<td>393.8</td>
<td>272.08</td>
</tr>
<tr>
<td>计算型 ecs.c5.3xlarge</td>
<td>12</td>
<td>24</td>
<td>3.73</td>
<td>1074</td>
<td>1074</td>
<td>912.9</td>
<td>590.7</td>
<td>408.12</td>
</tr>
<tr>
<td>计算型 ecs.c5.4xlarge</td>
<td>16</td>
<td>32</td>
<td>4.97</td>
<td>1432</td>
<td>1432</td>
<td>1217.2</td>
<td>787.6</td>
<td>544.16</td>
</tr>
<tr>
<td>计算型 ecs.c5.6xlarge</td>
<td>24</td>
<td>48</td>
<td>7.46</td>
<td>2148</td>
<td>2148</td>
<td>1825.8</td>
<td>1181.4</td>
<td>816.24</td>
</tr>
<tr>
<td>计算型 ecs.c5.8xlarge</td>
<td>32</td>
<td>64</td>
<td>9.94</td>
<td>2864</td>
<td>2864</td>
<td>2434.4</td>
<td>1575.2</td>
<td>1088.32</td>
</tr>
<tr>
<td>计算型 ecs.c5.16xlarge</td>
<td>64</td>
<td>128</td>
<td>19.89</td>
<td>5728</td>
<td>5728</td>
<td>4868.8</td>
<td>3150.4</td>
<td>2176.64</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc5.large</td>
<td>2</td>
<td>4</td>
<td>0.87</td>
<td>251</td>
<td>251</td>
<td>208.33</td>
<td>125.5</td>
<td>82.83</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc5.xlarge</td>
<td>4</td>
<td>8</td>
<td>1.74</td>
<td>502</td>
<td>502</td>
<td>416.66</td>
<td>251</td>
<td>165.66</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc5.2xlarge</td>
<td>8</td>
<td>16</td>
<td>3.49</td>
<td>1004</td>
<td>1004</td>
<td>833.32</td>
<td>502</td>
<td>331.32</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc5.3xlarge</td>
<td>12</td>
<td>24</td>
<td>5.23</td>
<td>1506</td>
<td>1506</td>
<td>1249.98</td>
<td>753</td>
<td>496.98</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc5.4xlarge</td>
<td>16</td>
<td>32</td>
<td>6.97</td>
<td>2008</td>
<td>2008</td>
<td>1666.64</td>
<td>1004</td>
<td>662.64</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc5.6xlarge</td>
<td>24</td>
<td>48</td>
<td>10.46</td>
<td>3012</td>
<td>3012</td>
<td>2499.96</td>
<td>1506</td>
<td>993.96</td>
</tr>
<tr>
<td>高主频计算型 ecs.hfc5.8xlarge</td>
<td>32</td>
<td>64</td>
<td>13.94</td>
<td>4016</td>
<td>4016</td>
<td>3333.28</td>
<td>2008</td>
<td>1325.28</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c2g1.large</td>
<td>2</td>
<td>8</td>
<td>8.68</td>
<td>2500</td>
<td>2375</td>
<td>1875</td>
<td>1125</td>
<td>750</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c4g1.xlarge</td>
<td>4</td>
<td>16</td>
<td>9.69</td>
<td>2790</td>
<td>2650.5</td>
<td>2092.5</td>
<td>1255.5</td>
<td>837</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c8g1.2xlarge</td>
<td>8</td>
<td>32</td>
<td>11.67</td>
<td>3360</td>
<td>3192</td>
<td>2520</td>
<td>1512</td>
<td>1008</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c16g1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>15.63</td>
<td>4500</td>
<td>4275</td>
<td>3375</td>
<td>2025</td>
<td>1350</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c16g1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>31.25</td>
<td>9000</td>
<td>8550</td>
<td>6750</td>
<td>4050</td>
<td>2700</td>
</tr>
<tr>
<td>GPU计算型 ecs.gn5i-c28g1.14xlarge</td>
<td>56</td>
<td>224</td>
<td>43.06</td>
<td>12400</td>
<td>11780</td>
<td>9300</td>
<td>5580</td>
<td>3720</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc5s.24xlarge</td>
<td>96</td>
<td>192</td>
<td>29.83</td>
<td>8592</td>
<td>8592</td>
<td>7303.2</td>
<td>4725.6</td>
<td>3264.96</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f5-c8f1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>10.5</td>
<td>5040</td>
<td>5040</td>
<td>5040</td>
<td>5040</td>
<td>5040</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f5-c16f1.16xlarge</td>
<td>64</td>
<td>256</td>
<td>21</td>
<td>10080</td>
<td>10080</td>
<td>10080</td>
<td>10080</td>
<td>10080</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f5-c32f1.32xlarge</td>
<td>128</td>
<td>512</td>
<td>42</td>
<td>20160</td>
<td>20160</td>
<td>20160</td>
<td>20160</td>
<td>20160</td>
</tr>
<tr>
<td>计算型弹性裸金属服务器 ecs.ebmc4.8xlarge</td>
<td>32</td>
<td>64</td>
<td>9.84</td>
<td>3150</td>
<td>3150</td>
<td>2677.5</td>
<td>1732.5</td>
<td>1197</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f3-c4f1.xlarge</td>
<td>4</td>
<td>16</td>
<td>7.312</td>
<td>3510</td>
<td>3510</td>
<td>2983.5</td>
<td>1930.5</td>
<td>3510</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f3-c8f1.2xlarge</td>
<td>8</td>
<td>32</td>
<td>8.375</td>
<td>4020</td>
<td>4020</td>
<td>3417</td>
<td>2211</td>
<td>4020</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f3-c16f1.4xlarge</td>
<td>16</td>
<td>64</td>
<td>10.5</td>
<td>5040</td>
<td>5040</td>
<td>4284</td>
<td>2772</td>
<td>5040</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f3-c16f1.8xlarge</td>
<td>32</td>
<td>128</td>
<td>21</td>
<td>10080</td>
<td>10080</td>
<td>8568</td>
<td>5544</td>
<td>10080</td>
</tr>
<tr>
<td>FPGA计算型 ecs.f3-c16f1.16xlarge</td>
<td>64</td>
<td>256</td>
<td>42</td>
<td>20160</td>
<td>20160</td>
<td>17136</td>
<td>11088</td>
<td>20160</td>
</tr>
<tr>
<td>大数据计算型 ecs.d3c.3xlarge</td>
<td>14</td>
<td>56</td>
<td>7.103</td>
<td>3409.6</td>
<td>3409.6</td>
<td>2898.16</td>
<td>1875.28</td>
<td>1295.65</td>
</tr>
<tr>
<td>大数据计算型 ecs.d3c.7xlarge</td>
<td>28</td>
<td>112</td>
<td>14.207</td>
<td>6819.2</td>
<td>6819.2</td>
<td>5796.32</td>
<td>3750.56</td>
<td>2591.3</td>
</tr>
<tr>
<td>大数据计算型 ecs.d3c.14xlarge</td>
<td>56</td>
<td>224</td>
<td>28.413</td>
<td>13638.41</td>
<td>13638.41</td>
<td>11592.65</td>
<td>7501.12</td>
<td>5182.59</td>
</tr>
<tr>
<td>大数据计算型 ecs.d2c.6xlarge</td>
<td>24</td>
<td>88</td>
<td>11.458</td>
<td>3300</td>
<td>3300</td>
<td>2805</td>
<td>1815</td>
<td>1254</td>
</tr>
<tr>
<td>大数据计算型 ecs.d2c.12xlarge</td>
<td>48</td>
<td>176</td>
<td>22.915</td>
<td>6600</td>
<td>6600</td>
<td>5610</td>
<td>3630</td>
<td>2508</td>
</tr>
<tr>
<td>大数据计算型 ecs.d2c.24xlarge</td>
<td>96</td>
<td>352</td>
<td>45.83</td>
<td>13200</td>
<td>13200</td>
<td>11220</td>
<td>7260</td>
<td>5016</td>
</tr>
</tbody>
</table>
<h2>阿里云计算型实例云服务器活动报价</h2>
目前阿里云在活动中推出了计算型c7和计算型c8y实例规格的计算型实例云服务器，配置最低为1核2G，最高为8核16G，新老用户购买计算型c7实例云服务器折扣为6折，老用户为7.2折，购买时长为1年，带宽1M-5M可选，而计算型c8y实例云服务器为新老用户同享，折扣统一为7.2折，购买时长最高为5年，不限制带宽容量，详细活动价格如下：
1、计算型c7实例云服务器活动价格
<table>
<thead>
<tr>
<th>云服务器实例</th>
<th>配置</th>
<th>带宽</th>
<th>系统盘容量</th>
<th>1M带宽活动价格</th>
<th>2M带宽活动价格</th>
<th>3M带宽活动价格</th>
<th>4M带宽活动价格</th>
<th>5M带宽活动价格</th>
<th>折扣</th>
<th>限购条件</th>
</tr>
</thead>
<tbody>
<tr>
<td>计算型c7</td>
<td>2核4G</td>
<td>1M-5M</td>
<td>40G ESSD云盘</td>
<td>1718.61元/1年</td>
<td>1884.21元/1年</td>
<td>2064.21元/1年</td>
<td>2244.21元/1年</td>
<td>2453.01元/1年</td>
<td>6折</td>
<td>新用户</td>
</tr>
<tr>
<td>计算型c7</td>
<td>4核8G</td>
<td>1M-5M</td>
<td>40G ESSD云盘</td>
<td>3127.61元/1年</td>
<td>3293.21元/1年</td>
<td>3473.21元/1年</td>
<td>3653.21元/1年</td>
<td>3862.01元/1年</td>
<td>6折</td>
<td>新用户</td>
</tr>
<tr>
<td>计算型c7</td>
<td>8核16G</td>
<td>1M-5M</td>
<td>40G ESSD云盘</td>
<td>5945.63元/1年</td>
<td>6111.23元/1年</td>
<td>6291.23元/1年</td>
<td>6471.23元/1年</td>
<td>6680.03元/1年</td>
<td>6折</td>
<td>新用户</td>
</tr>
<tr>
<td>计算型c7</td>
<td>2核4G</td>
<td>1M-5M</td>
<td>40G ESSD云盘</td>
<td>2129.41元/1年</td>
<td>2364.01元/1年</td>
<td>2619.01元/1年</td>
<td>2874.01元/1年</td>
<td>3169.81元/1年</td>
<td>7.2折</td>
<td>老用户</td>
</tr>
<tr>
<td>计算型c7</td>
<td>4核8G</td>
<td>1M-5M</td>
<td>40G ESSD云盘</td>
<td>3820.22元/1年</td>
<td>4054.82元/1年</td>
<td>4309.82元/1年</td>
<td>4564.82元/1年</td>
<td>4860.62元/1年</td>
<td>7.2折</td>
<td>老用户</td>
</tr>
<tr>
<td>计算型c7</td>
<td>8核16G</td>
<td>1M-5M</td>
<td>40G ESSD云盘</td>
<td>7201.83元/1年</td>
<td>7436.43元/1年</td>
<td>7691.43元/1年</td>
<td>7946.43元/1年</td>
<td>8242.23元/1年</td>
<td>7.2折</td>
<td>老用户</td>
</tr>
</tbody>
</table>
精准价格及购买直达：阿里云服务器降价活动
2、计算型c8y实例云服务器活动价格
<table>
<thead>
<tr>
<th>云服务器实例</th>
<th>配置</th>
<th>带宽</th>
<th>系统盘容量</th>
<th>活动价格1年</th>
<th>活动价格2年</th>
<th>活动价格3年</th>
<th>活动价格4年</th>
<th>活动价格5年</th>
<th>折扣</th>
<th>购买资格</th>
</tr>
</thead>
<tbody>
<tr>
<td>计算型c8y</td>
<td>1核2G</td>
<td>1M起</td>
<td>40G起 ESSD云盘</td>
<td>992.11元</td>
<td>1727.72元</td>
<td>2147.76元</td>
<td>2586.93元</td>
<td>3003.03元</td>
<td>7.2折</td>
<td>新老用户同享</td>
</tr>
<tr>
<td>计算型c8y</td>
<td>2核4G</td>
<td>1M起</td>
<td>40G起 ESSD云盘</td>
<td>1545.63元</td>
<td>2650.24元</td>
<td>3231.73元</td>
<td>3755.45元</td>
<td>4233.06元</td>
<td>7.2折</td>
<td>新老用户同享</td>
</tr>
<tr>
<td>计算型c8y</td>
<td>4核8G</td>
<td>1M起</td>
<td>40G起 ESSD云盘</td>
<td>2652.65元</td>
<td>4495.28元</td>
<td>5399.65元</td>
<td>6092.51元</td>
<td>6693.12元</td>
<td>7.2折</td>
<td>新老用户同享</td>
</tr>
</tbody>
</table>
精准价格及购买直达：阿里云服务器百亿补贴活动
<h2>选择推荐：</h2>
以上就是阿里云计算型实例云服务器收费标准及最新活动价格，其中计算型c7实例属于第七代云服务器，计算型c8y实例属于第八代云服务器，普通用户选择计算型c7就可以了，如果对云服务器有更高的计算、存储、网络性能，则可以考虑计算型c8y实例云服务器。从价格角度来选择的话，计算型c7实例的新用户价格是最划算的，新用户推荐优先考虑。
最后：如果活动内的配置不是自己想要的，推荐先通过阿里云官方云小站平台领取代金券，在结算云服务器订单时，勾选使用代金券，这样购买更便宜。
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