3090显卡怎么对比？

核心规格：

拥有 10496 个 CUDA 核心。
24GB GDDR6X 显存，带宽为 936GB/s。
基础频率为 1.47 GHz，加速频率为 1.86 GHz

搭载 82 个第二代 RT Core，光线追踪性能出色。
第三代 Tensor Core 提供高效的 DLSS 性能，但在某些情况下提升幅度不如预期。

利用 RTX 3090 的强大计算能力：

import torch
import torch.nn as nn
import torch.optim as optim
from torchvision import datasets, transforms

# 定义一个简单的神经网络
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.dropout1 = nn.Dropout2d(0.25)
        self.dropout2 = nn.Dropout2d(0.5)
        self.fc1 = nn.Linear(9216, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.ReLU()(x)
        x = self.conv2(x)
        x = nn.ReLU()(x)
        x = nn.MaxPool2d(2)(x)
        x = self.dropout1(x)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.ReLU()(x)
        x = self.dropout2(x)
        x = self.fc2(x)
        output = nn.LogSoftmax(dim=1)(x)
        return output

# 实例化网络
net = Net()

# 加载数据集
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307,), (0.3081,))
])
trainset = datasets.MNIST('~/.pytorch/MNIST_data/', download=True, train=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

# 训练网络
for epoch in range(10):  # loop over the dataset multiple times
    running_loss = 0.0
    for i, data in enumerate(trainloader, 0):
        inputs, labels = data
        optimizer.zero_grad()
        outputs = net(inputs)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch + 1}, Loss: {running_loss / len(trainloader)}')