How much RAM makes sense for a bioinformatics PC?

For a general-purpose research machine, 64 GB is a reasonable starting point, while 128 GB gives more room for workflows that keep large datasets in memory or run several steps at once. A compressed dataset’s size doesn’t tell you its peak memory use, so check the pipeline’s requirements or monitor a representative run before deciding.

Before spending the whole budget on a workstation, ask a nearby university or research group whether they offer access to shared computing. Bioinformatics jobs are often run in batches on managed systems, and that can be a better fit for occasional jobs that need more resources than a desktop has.

Don’t assume a big GPU is necessary just because the data is biological. The right parts depend on the actual tools: some tasks benefit more from CPU performance, memory capacity, storage speed, or memory bandwidth. Find out what your pipeline uses, then build around that rather than buying a high-end graphics card by default.

If you’re leaning toward 128 GB, check the motherboard’s supported memory capacity and the cost of a compatible kit where you live. You can also start with 64 GB if the board leaves room to expand, but verify that adding more later won’t require replacing the initial modules.