What is the reason of RTX 3090 is slower than 3060?

Question

I run the exactly same code for my 2 computers.

Both of them are installed of tensorflow-gpu==2.7.0 | CUDA = 11.2 | cudnn = 8.1.

For simple Autoencoder code. (Epoch : 300)

3090 takes 1m 57.4s and 3060 takes 52.9 s.

3090 memory is 24 GB and 3060 memory is 12 GB. (I used both of them full memory and I can see it uses fully by task manager)

What should I do?

Until now I can't understand my situation.

Pls give me some advice what should I have to check.

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<1> RTX 3090 computer specification

(1) CPU : Intel Xeon Silver 4310 CPU @ 2.10GHz

(2) Memory : 128 GB

<2> RTX 3060 computer specification

(1) Intel Xeon W-2245 CPU @ 3.90GHz

(2) Memory : 128 GB

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3060 computer uses CPU 24~27% (4.49GHz) | memory 9%

3090 computer uses CPU 11~15% (1.38~1.56GHz) | memory 7%

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Code is below

    input_data = keras.Input(shape=(20,))
    encoded = Dense(15, activation='relu')(input_data)
    encoded = Dense(5, activation='relu')(encoded)
    decoded = Dense(15, activation='relu')(encoded)
    decoded = Dense(20, activation='relu')(decoded)

    autoencoder = keras.Model(input_data, decoded)
    autoencoder.compile(optimizer='adam', loss='mse')

    autoencoder.fit(sc_x_train, sc_x_train,
                    epochs=300,
                    batch_size=16,
                    shuffle=True,
                    verbose=0)

    encoder = keras.Model(input_data, encoded)
    encoder.compile(optimizer='adam', loss='mse')