cluster of a large dataset

I’ve been running through the MiSeq SOP with my data. After performing dist.seqs I get a file that’s nearly 90GB. When attempting to cluster I keep getting this error:

mothur > dist.seqs(fasta=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.fasta, cutoff=0.20, processors=3)

Using 3 processors.

Output File Names:

It took 108824 to calculate the distances for 122573 sequences.

mothur > cluster(column=stability.trim.contigs.good.unique.good.filter.unique.precluster.pick.pick.dist, count=stability.trim.contigs.good.unique.good.filter.unique.precluster.uchime.pick.pick.count_table)
Reading matrix: |||||||||||||||||||||||||||||||||||||||||||||||||||[ERROR]: std::bad_alloc has occurred in the SparseDistanceMatrix class function addCell. This error indicates your computer is running out of memory. This is most commonly caused by trying to process a dataset too large, using multiple processors, or a file format issue. If you are running our 32bit version, your memory usage is limited to 4G. If you have more than 4G of RAM and are running a 64bit OS, using our 64bit version may resolve your issue. If you are using multiple processors, try running the command with processors=1, the more processors you use the more memory is required. Also, you may be able to reduce the size of your dataset by using the commands outlined in the Schloss SOP, If you are uable to resolve the issue, please contact Pat Schloss at, and be sure to include the mothur.logFile with your inquiry.

I have attempted these two steps with as little as 1 processor and as many as 8 (I have 8GB of RAM) but as of yet without any success. I have also reduced the size of the *.dist file by renaming all of the sequences using shorter names.
What am I doing wrong?

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