Text Topic Modeling with LDA operator

I am trying to figure out how the LDA operator works. I have a data file on excel with 630 rows of text data. I  ran this through Process Documents from Data to generate the 'term occurrence' word vector as stated in the LDA manual. However when I feed this into the LDA operator, after a while I simply get an out-of-memory error. I've tried it with 4GB of RAM and 16 GB of RAM. What am I doing wrong? Attached is the xml. Thank you.

<?xml version="1.0" encoding="UTF-8"?><process version="7.5.001">
  <context>
    <input/>
    <output/>
    <macros/>
  </context>
  <operator activated="true" class="process" compatibility="7.5.001" expanded="true" name="Process">
    <process expanded="true">
      <operator activated="true" class="read_excel" compatibility="7.5.001" expanded="true" height="68" name="Read Excel" width="90" x="112" y="85">
        <parameter key="excel_file" value="C:\Users\Pari\OneDrive\ADMIN onedrive\Projects\Valbot\Rel8ed\Valbot RapidMiner-Export-Import\Data\Landscape Text from Blogs.xlsx"/>
        <parameter key="imported_cell_range" value="A1:A637"/>
        <parameter key="first_row_as_names" value="false"/>
        <list key="annotations">
          <parameter key="0" value="Name"/>
        </list>
        <list key="data_set_meta_data_information">
          <parameter key="0" value="backyard landscaping ideas oakville ontario backyard landscaping ideas oakville ontario backyard la….true.text.attribute"/>
        </list>
      </operator>
      <operator activated="true" class="text:process_document_from_data" compatibility="7.5.000" expanded="true" height="82" name="Process Documents from Data" width="90" x="246" y="85">
        <parameter key="vector_creation" value="Term Occurrences"/>
        <parameter key="prune_method" value="absolute"/>
        <parameter key="prune_below_absolute" value="50"/>
        <parameter key="prune_above_absolute" value="600"/>
        <list key="specify_weights"/>
        <process expanded="true">
          <operator activated="true" class="text:transform_cases" compatibility="7.5.000" expanded="true" height="68" name="Transform Cases" width="90" x="45" y="34"/>
          <operator activated="true" class="text:tokenize" compatibility="7.5.000" expanded="true" height="68" name="Tokenize" width="90" x="179" y="34"/>
          <operator activated="true" class="text:filter_stopwords_english" compatibility="7.5.000" expanded="true" height="68" name="Filter Stopwords (English)" width="90" x="313" y="34"/>
          <operator activated="true" class="text:filter_by_length" compatibility="7.5.000" expanded="true" height="68" name="Filter Tokens (by Length)" width="90" x="447" y="34">
            <parameter key="min_chars" value="3"/>
          </operator>
          <connect from_port="document" to_op="Transform Cases" to_port="document"/>
          <connect from_op="Transform Cases" from_port="document" to_op="Tokenize" to_port="document"/>
          <connect from_op="Tokenize" from_port="document" to_op="Filter Stopwords (English)" to_port="document"/>
          <connect from_op="Filter Stopwords (English)" from_port="document" to_op="Filter Tokens (by Length)" to_port="document"/>
          <connect from_op="Filter Tokens (by Length)" from_port="document" to_port="document 1"/>
          <portSpacing port="source_document" spacing="0"/>
          <portSpacing port="sink_document 1" spacing="0"/>
          <portSpacing port="sink_document 2" spacing="0"/>
        </process>
      </operator>
      <operator activated="true" class="corpus_linguistics_plugin_LDA:lda_topic_model" compatibility="1.1.001" expanded="true" height="145" name="Latent Dirichlet Allocation" width="90" x="447" y="85"/>
      <connect from_op="Read Excel" from_port="output" to_op="Process Documents from Data" to_port="example set"/>
      <connect from_op="Process Documents from Data" from_port="example set" to_op="Latent Dirichlet Allocation" to_port="example set of documents as Bag-of-Words vectors with term occurrences"/>
      <connect from_op="Latent Dirichlet Allocation" from_port="cluster model" to_port="result 1"/>
      <portSpacing port="source_input 1" spacing="0"/>
      <portSpacing port="sink_result 1" spacing="0"/>
      <portSpacing port="sink_result 2" spacing="0"/>
    </process>
  </operator>
</process>