Tensorflow for Node GPU doesn't release memory

Using @tensorflow/tfjs-node-gpu 3.0.0 and CUDA 10.0 on Ubuntu 20.04

// Prepares and returns 4GB data
async function prepareData () {
  return {
    inputs: inputTensor,
    outputs: outputTensor
  }
}

export async function train () {
  const { inputs, outputs } = await prepareData()
  const model = getModel()

  await model.fit(inputs, outputs, {
    validationSplit: 0.2,
    epochs: EPOCHS,
    shuffle: true
  })

  await model.save('file://model')
}

When calling the train method from somewhere else in my running Node process, 4GB of memory is allocated on the GPU, inspected with nvidia-smi. When the training ends, the memory is never released.

I have tried tf.dispose(), tf.disposeVariables, wrapping in tf.engine().startScope()/endScope(). The only thing that frees the memory is killing the containing Node process (which is not very practical since its a web server).

Is there any way to clear the allocated memory without killing the Node process?