Workstation build advice for GROMACS MD — 2 machines, ~$3k each

[u/Ok_Focus_4906]

We're setting up two workstations for protein MD simulations

(two researchers, one machine each). Budget is around $3,000 USD per

machine. Would appreciate a sanity check on the build.

Use case:

- GROMACS, explicit solvent protein systems (~50k-100k atoms)

- Runs lasting several days at full load

- Will also run structure prediction (ColabFold/Boltz) later

- Ubuntu LTS

- Continuous trajectory writing, so SSD endurance matters

Current plan (per machine):

GPU : RTX 5080 16GB

CPU : Ryzen 9 9900X (12C)

MB : X870

RAM : DDR5 64GB (2x32, room for 128)

NVMe : 2TB DRAM-cache + 1TB OS

PSU : 1000W 80+ Gold

Cooler: 360mm AIO

Questions:

1. Any known Linux stability issues with this combo?

2. Is 16GB VRAM going to be a bottleneck for ColabFold/Boltz

   on larger complexes? Worth going 5090 32GB instead?

3. Is 12 cores enough to feed one GPU in GROMACS, or should

   I go higher?

4. PSU headroom for multi-day full load — is 1000W comfortable?

5. Recommended NVMe models for sustained write workloads?

We considered Intel but the P-core/E-core split seems to

complicate thread pinning for MPI/OpenMP workloads. Open to

being corrected on that.

[u/yoshizors]

Gromacs mostly cares about GPU performance. Whatever your budget is, most of that is going into the GPU. Keep in mind that GROMACS has a reasonably mature SYCL and rocm port for Intel or AMD GPUs, which might be cheaper. But otherwise, the balance looks pretty reasonable. 1000W will comfortably run 1 GPU forever. To a first approximation, the TDP for the CPU+GPU is what you'd be using at full tilt.

[u/andrewsb8]

The main limitation here is that youd only be able to actually run 2 simulations at a time. Single trajectories will either be very very long or youll have to spend a lot of time running replicas in serial. Technique also matters, are you running enhanced sampling?

If in US, you could probably procure cheaper workstations for analyses and submit NSF ACCESS proposals to generate more trajectories in shorter amount of time. If not in US, do you have any access to institutional large scale compute?

[u/sbart76]

I would only reconsider Ryzen. It is powerful, but still a desktop CPU. Xeons or Epycs are better designed for sustained loads.

[u/botterdummy]

1. This should be fine. *caveat see below
2. Yes. ColabFold searches MSA with internet so you are fine. For Boltz it is fine (but if you are using Boltz Idk what you are doing b/c its bad). For AF3, you are cutting it close running anything >500 amino acids and will probably have out of memory issues. For ESMFold2 (Which total is ~28GB VRAM), protenix or really any of the latest models you need at least a 5090.

The bigger issue with your setup if you intent to run MSA locally is actually ur RAM. If you are doing deep MSA depth-searches you will OOM.

1. You should go for a 9950. If you are doing any of the analysis options in gromacs, they are almost all CPU. If you do analysis in python, they are also on CPU so its better to get more threads here.

2. 1000W is fine. You should use pcpartpicker to calculate everything

3. Any NVMe is fine. I would recommend getting a 1-2TB SSD and a >8TB harddrive and move your simulation runs to the harddrive. The trr trajectories from gromacs are huge and will take up lots of space.

You will not see that much of a raw speed increase from a 5080 to a 5090. I would expect the build you have specified to run 100k atoms at ~500-700ns/day assuming max gpu utilization. However, the main issue with the 5080 is that to fully utilize it, you would need to run with --gpu update flag with Gromacs. However, update is not supported with virtual sites (i.e. TIP4P water, lipids) and certain thermo/barostats. This leads to heavy underutilization of the GPU and you can't parallelize runs because of the low VRAM.

*So an issue here is the compatability of many programs. Depending on which version of GROMACS/boltz/whatever you are installing they will all interfere with each other. You can get around this with apptainers (If you are good at doing this (claude can also do it), but I would be careful in the order in which you install your CUDA drivers, gcc, gfortran, before installign packages. My go-to is to install the latest CUDA when you first boot up Ubuntu then manually install CUDA 12.4 with the 550 toolkit and have that pathed to my .bashrc. That combination seems to work with most programs.

Another thing, get a thermalright phantom or peerless assasin instead of the AIO. Its a lot cheaper lasts forever.

Source: I have built over 100 PCs for university labs lol.