Workstation build for CPU-heavy scientific computing: $6800 grant, 128–256 GB RAM target

[u/Dependent-Mud-6146]

Hi all,

I recently received a small grant of around $6800 to buy a workstation for my lab at the university. I work in computational engineering / numerical methods, mainly CPU-based simulations and algorithms.

I know this is not a huge budget for a high-performance workstation, but I see it as a starting point to slowly build the lab. I’m based in a small island state, so I also need to account for shipping/import costs, meaning the actual budget for the machine itself will probably be a bit less.

At the moment, my work is much more CPU/RAM-heavy than GPU-heavy. So my main requirement is to get as much RAM as possible. I would like to start with at least 128 GB RAM, but if there is a realistic way to get 256 GB within this budget, that would be ideal.

For the CPU, I was thinking along the lines of an AMD Ryzen Threadripper, but I’m open to suggestions. I’m not sure whether it is better to go for a newer/lower-end Threadripper, older higher-core-count workstation parts, or even something else entirely.

For the GPU, I don’t need anything very powerful right now. A basic GPU would probably be enough, as long as the system can be upgraded later. In the future, I may have students working on parallelized versions of the codes, GPU acceleration, or machine learning, but that is not the immediate priority.

A few questions:

1. What kind of workstation configuration would you recommend for this budget?
2. Should I prioritize CPU cores, RAM capacity, memory bandwidth, or platform expandability?
3. Is Threadripper the right direction, or should I consider EPYC / Xeon / used workstation hardware?
4. What would be the best way to make the system expandable in the future?
5. If I get additional small grants later, would it make more sense to upgrade this machine with more RAM/GPU, or start adding small compute nodes?

Initially, the workstation will probably be used by two people. Later, after upgrades, it may support more students in the lab.

Any advice on practical configurations, pitfalls, or good upgrade paths would be appreciated.

[u/jeffscience]

I regret to inform you that you have picked the worst time in at least a generation to buy DRAM.

[u/sob727 → u/jeffscience]

$6800 is the budget for 192GB of DDR5 ECC

[u/VegGrower2001]

My first piece of advice is to talk to your local HPC or IT team. They will surely be able to offer advice, advise on local policies etc. And they will likely have purchasing relationships that allow them to get good deals. Don't be isolated - talk to your friendly IT team!

[u/four_reeds → u/VegGrower2001]

+1

Depending on where you live in the world and, possibly with whom you collaborate, there may be free or low-bar-to-entry systems that will outperform anything you can install in your office or lab.

Don't forget that high-end machines often require high-end physical infrastructure. Your office/lab might need additional air conditioning, soundproofing, larger network throughput than is often installed outside of data centers and they may require "conditioned" electrical power.

You department IT people should be your first contact. If your campus has an HPC center, they should be your first or second contact.

I work for a large US university. It has been two years since I was active in our local HPC systems but for your budget (assuming it can be spent in this way) you could buy a very good 30+ core node in your own "queue" and have shared (free) access to a major dedicated GPU cluster. All that professionally administered and housed in specialty equipped data centers.

Plus, they would tell you about the ACCESS program.

[u/VegGrower2001 → u/four_reeds]

100% agree 👍🏻

[u/camelCase609 → u/[deleted]]

I do think if the trust with IT is established then certainly get their input. Something to be said about coming here before there.

[u/jmakov]

2x epyc 9755 es + mobo from ebay (4800$). RAM might cost you more mind you

[u/Primary_Olive_5444]

https://www.youtube.com/watch?v=SDSZb0xwlRk&t=166s

From Wendell at level one tech, recent upload.
Intel Xeon 6 658X with 24 cores

[u/breagerey]

Before you do anything explore the HPC and IT resources at your university and the state.
You may find what you need is already there - and if not you will likely find people in a good position to help with either planning or procurement.

In my state there are HPC resources available to students/researchers affiliated with an edu in the state - so you may be overlooking available resources even if your university doesn't have HPC.

[u/violets_moon → u/breagerey]

There's also Jetstream2

[u/tecedu]

Wrong sub but anyways we can answer.

First of all which country?

Have you done any benchmarking?

Have you tried any cloud providers?

Have you talked to your uni's IT department and do they know a resller?

If we go with the current assumptions

What kind of workstation configuration would you recommend for this budget?

No workstation, get an old server based on your requirments, this isnt the time where you can customise, 64gb ddr5 ecc sticks are going 1k

Should I prioritize CPU cores, RAM capacity, memory bandwidth, or platform expandability?

This all depends on what you, but general preference is keep CPU static, have space for more ram which can already add RAM capacity and bandwidth. Your platofrm expandiblity depends on your machine and your cpu and your mother. A 3 year old epyc server can accomodate 8 expansion cards if needed

Is Threadripper the right direction, or should I consider EPYC / Xeon / used workstation hardware?

Since this is multiple use, don't go threadripper? I dont know how you are planning to have it usable for multiple users, but you if its a desktop workstation then its going to be PITA, get something rack mounted, and you connected remote. Preference is Epyc > intel however depends on pricing and workloads.

What would be the best way to make the system expandable in the future?

Pick the correct vendor

If I get additional small grants later, would it make more sense to upgrade this machine with more RAM/GPU, or start adding small compute nodes?

Depends on your project grant and type of computation, if its something you can use mpi with then more compute nodes or else big fit single node

[u/Chance-Pineapple8198]

Even when looking at a CPU/RAM-heavy workload, the answer to 2 is going to depend on if you always plan to target the same kind of simulations (I'm in a different boat with my home rack, because I don't have one specific thing I want to run, and that makes it a little harder to compare configurations) and if, right now, those seem to be more constrained by the CPU, the memory bandwidth, or the RAM. This is very code-dependent, but you can get some rough idea of these things from whatever you're running on now by doing strong and weak scaling studies.

Also, when you say 'parallelized', do you only mean that you don't currently have GPU parallelization, or do you also not have CPU parallelization? If the latter, that should probably be the target before building a workstation, because the only benefit you'd see now is being able to run multiple, independent simulations serially (and probably some speedup from the better single cores), and you won't be able to do the scaling studies that might help inform your purchase.

[u/MagicalPC]

I agree with those saying to reach out to your local IT and other universities for guidance.

However, I would also add that you will need to get specific about your workloads and resource requirements. A great place to start is a basic benchmark. Build something cheap or free and use it as a frame of reference for your research.

You can build an X99 system (Xeon E5 v4) with 20+ cores and a few sticks of DDR4 2400 memory using bundles from Aliexpress for around $500 (not including GPU or storage). Or even make a cluster of mini PCs from ebay for the same price.

These would give you an idea of which components are the bottlenecks under different workloads and where you can benefit the most from upgrades.

I have done this with my own money and just sell the old hardware when I no longer need it and reinvest that into upgrades.

[u/rocket2267]

Are your problems computationally bound, or memory bound?

[u/DrJoeVelten]

Quick check: Did your school's IT department already surplus all of their windows 11 incompatible computers? Because I had a student do a senior design project making a Beowulf cluster for cpu/ram heavy work for the princely sum of $200, including a wire rack and a switch, with the help of posters from here and 17 computers that were about to be sold for scrap.

It's a heck of a bang for your buck, and you can build that, and then use that for early simulation data while you get your stuff ordered and shipped in. That, and you can have undergrads bang on it and you won't cry if they screw something up.

[u/Zealousideal-War6372]

What workloads does it run ?

[u/MentalStatusCode410]

Better value from something like a 9950X compared to threadripper, and consider an FPGA.

Threadripper makes sense if you're needing the PCIE lanes.

[u/kidflashonnikes]

okay so I can help. I work at one of the largest AI companies, one of the big 3, and maybe this can help. I have a personal set up, of 4 RTX PRO 6000s, 1 TB of DDR5 ECC RAM (kingston 5600), and 16 TB of nvme, and a 96 core CPU working. We have already seen prototypes for CPUs and GPUs that will be released in 2027, as well as the prototype PCBs for the RTX 6000 series, specifically the RTX 6090. I can tell you with 100% confidence that your set up will be an absolute waste of money and time for research. Our model release roadmap for 2027, but mainly 2028, will usher in a level of tech that I still find hard to believe is even real. I would focus more on the GPU side, as CPUs will become irrelevent in the near future, from what we have tested, and seen. GPUs and CPUs are going to change big time. The neural net will be the foundation - the classic computer will be the UI, right now, its the other way around, but that is where we are heading.

[u/FalconX88 → u/kidflashonnikes]

okay so I can help.

You clearly can't. OP stated that their workload is CPU heavy and you are talking about RTX Pro 6000s and that GPU is the future, clearly not understanding the type of workload OP is talking about.

I would focus more on the GPU side, as CPUs will become irrelevent in the near future,

Hillarious comment because even the AI companies are now saying that they will need at least one CPU rack for every X (often cited 2-4) GPU racks.

[u/targetDrone → u/kidflashonnikes]

I work at an HPC centre with clusters that can and do do AI but mostly do scientific and engineering compute. AI models are useful and are getting better - weather simulation and computational chemistry are especially good, but +60% of our users' jobs still don't even use GPUs despite the decade-long push and our dev's assistance to port code, so CPUs are not a waste of time for us. For our users a moderate number of fast cores per node is optimum.

We are also privy to upcoming hardware and have a different point of view to you. You are right that low-precision AI blocks are coming to everything, which is great if you're running ai models, but pretty useless for compute. In fact last-gen Nvidia cards are possibly going to be better for HPC compute than their next-gen AI-focussed ones. Or use AMD Instinct which still has strong fp64, if you can sell that to the users...

As to the OP's question, I would also recommend using your organisation's IT facility. At worst you can leverage their vendor discounts, at best they 're not discovering a rogue device on their net and coming after you. Best case they let you buy into an existing cluster that can offer you more when you need it.

[u/divided_capture_bro]

If you don't need GPU or unified memory then the cheapest approach at the moment would be to get a horde of refurbished Dell 7070s and make a mini-cluster.

Here is an easy example of not even the cheapest machines:

https://a.co/d/0dRVwWbd

These have 6 cores and 32GB RAM a pop, so supposing you buy 10 at $375 you'd have 60 cores and 320GB RAM. With another $2700 you could buy an ASUS ROG laptop with its own 128GB RAM and a good enough graphics card integration to use as the mobile terminal and major node.

https://a.co/d/05BvmZWa

Alternatively, here is a single machine build with 22 cores and 256GB RAM for around $2500 if you need a single machine on a budget. No graphics card included.

https://pcpartpicker.com/list/VKLbh9

What to get really depends on what you will be doing. If it is embarrassingly parallel work, then mini-cluster - especially if you also need the higher core count that comes with it. If your computation is memory bound then you'll probably be better off with a single machine or smaller number of machines due to latency and bandwidth.

So what are you planning on doing?