When running the same code on my m1 Mac with tensorflow-metal vs in a google collab I see a problem with results. The code: https://colab.research.google.com/drive/13GzSfToUvmmGHaROS-sGCu9mY1n_2FYf?usp=sharing import tensorflow as tf import numpy as np import pandas as pd # Setup model input_shape = (10, 5) model_tst = tf.keras.Sequential() model_tst.add(tf.keras.Input(shape=input_shape)) model_tst.add(tf.keras.layers.LSTM(100, return_sequences=True)) model_tst.add(tf.keras.layers.Dense(2, activation="sigmoid")) model_tst.summary() optimizer = tf.keras.optimizers.Adam(learning_rate=0.01) loss = tf.keras.losses.BinaryCrossentropy(from_logits=False) model_tst.compile( loss=loss, optimizer=optimizer, # metrics=[tf.keras.metrics.BinaryCrossentropy() metrics=["mse" ] ) # Generate step data random_input = np.ones((11, 10, 5)) random_input[:, 8:, :] = 99 # Predictions random_output2 = model_tst.predict(random_input, batch_size=1)[0, :, :].reshape(10, 2) random_output3 = model_tst.predict(random_input, batch_size=10)[0, :, :].reshape(10, 2) # Compare results diff2 = random_output3 - random_output2 pd.DataFrame(diff2).T Output on Mac: Output on google collab: If I reduce the number of nodes in the LSTM I can get the problem to disappear: import tensorflow as tf import numpy as np import pandas as pd # Setup model input_shape = (10, 5) model_tst = tf.keras.Sequential() model_tst.add(tf.keras.Input(shape=input_shape)) model_tst.add(tf.keras.layers.LSTM(2, return_sequences=True)) model_tst.add(tf.keras.layers.Dense(2, activation="sigmoid")) model_tst.summary() optimizer = tf.keras.optimizers.Adam(learning_rate=0.01) loss = tf.keras.losses.BinaryCrossentropy(from_logits=False) model_tst.compile( loss=loss, optimizer=optimizer, # metrics=[tf.keras.metrics.BinaryCrossentropy() metrics=["mse" ] ) # Generate step data random_input = np.ones((11, 10, 5)) random_input[:, 8:, :] = 99 # Predictions random_output2 = model_tst.predict(random_input, batch_size=1)[0, :, :].reshape(10, 2) random_output3 = model_tst.predict(random_input, batch_size=10)[0, :, :].reshape(10, 2) # Compare results diff2 = random_output3 - random_output2 pd.DataFrame(diff2).T -> outputs are the same in this case. I guess this has to do with how calculations are getting passed to Apple silicon. Any debugging steps I should try to result this problem? Info: I setup tensor flow using the following steps: https://developer.apple.com/metal/tensorflow-plugin/ When running I get this output showing that the GPU plugins are being used