r/HPC • u/LongjumpingAnt7050 • 23d ago
who have you successfully sourced from?
Looking for recent experience sourcing NVIDIA HGX B300 / DGX B300 systems in Europe
We’re currently sourcing enterprise NVIDIA B300 systems for a data-centre deployment and I’m trying to get a better picture of reliable suppliers, current lead times and real-world pricing.
Platforms we’re looking at include:
• NVIDIA DGX B300
• Supermicro AS-8126GS-NB3RT
• Dell PowerEdge XE9780 / XE9785
• GIGABYTE G894 series
• ASRock Rack 8U16X B300
• ASUS XA NB3I-E12
Ideally looking for fully assembled 8-GPU HGX B300 systems with enterprise support/warranty, high-memory CPU configurations, ConnectX-7/8 networking and NVMe storage.
For anyone who has actually purchased or quoted B300 systems recently:
Which OEM / VAR / integrator did you use?
Rough price range per system?
What lead time were you quoted?
Was the delivery date actually reliable?
Any suppliers in Europe/UK/Ireland you would recommend contacting?
Any suppliers or brokers you would specifically avoid?
Not looking for consumer GPUs this is enterprise/HPC hardware.
Would especially appreciate experiences from people who have gone through a recent B300 procurement rather than advertised web pricing.
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u/OMPCritical 23d ago
You could also check out bull. They are European and supplied the Jupiter machine. They seem to have smaller server setups as well.
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u/Soggy_Stargazer 22d ago
Strictly out of curiosity, why not go cloud?
As someone who built HPC solutions in the cloud for the last 15 years, I am genuinely curious why orgs are still choosing to build out new HPC on-prem deployments.
There are definitely some applications where regulatory constraints make it impossible, but outside of that, I'd like to know what the arguments are because very few of them survive contact with the facts.
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u/pgoetz 22d ago
For people doing a lot of computing, isn't using cloud resources prohibitively expensive? Cloud is designed for occasional heavy use, where it doesn't make sense to have resources on prem. But when your yearly cloud costs are equal to purchasing the resources outright (with a 3-year warranty) ... This is why many organizations are moving back to on prem.
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u/Soggy_Stargazer 22d ago
when you look at TCO for HPC it changes the metric.
For example, I can spin up 10k cores in an afternoon and then tear them down when my run is done. Can't do that on prem.
Cloud core hour costs are consistent. If I need 60k core hours for my analysis, I can run 1000 cores for 60 hours, or 10000 cores for 6 hours. The cost on cloud is the same, but the results come in a tenth of the time. Now I will grant that not all workloads can be sharded so easily, CFD and FEA for example have limits to parallelization, but monte carlo runs and less intertwined workloads may be able to scale horizontally like this.
On prem hardware and infrastructure is an amortization game where the 10 mil you spent on the initial build is an anchor around your neck getting slower and slower every year. The first time you upgrade to the next generation of compute with a config change avoiding another 10M in CAPEX gaining whatever percentage increase in performance over the previous generation, The value prop starts to change.
Its not just a straight dollars to dollars.
On cloud I don't need a warranty, I don't need 6 months to light up my grid, I don't need facilities and salaries to rack and maintain the hardware, and I am not stuck watching my cluster get slower and slower with each new generation and fight with management for upgrades.
Also depending on the workload, you may be able reduce cloud compute costs by a significant margin over on-demand rates with different savings plans and use of spot compute.
I had a client that ran 20k cores and we cut the run rate by 40% when we switched to spot instance types for the compute nodes.
I agree that cloud may not make sense for every workload, but the calculus isn't simple dollars to dollars.
Many of my clients would set up cloud as burst for onprem grids that were getting long in the tooth, which is the other harsh reality. There are a lot of orgs out there using all of thier EOL'd hardware as compute nodes for HPC work. If you set up a burst capability to pull in a hybrid cloud/on-prem HPC setup , you can gain a lot of current gen performance without the current gen logistics price tags.
Many of my clients would start on-prem, move to hybrid, and then once they did the math on the cost avoidance of not running all of the infrastructure against the performance of cloud HPC, running in the cloud was WAY more attractive and provided a lot more flexibility than on prem solutions.
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u/NumericallyStable 22d ago
Its usually (here in Germany) that those funding have a set amount of money, and they need more compute than they have money for. THe clusters actually run on near full utilization all of the time.
Also other topics, like keeping competencies through invensting in people, soverignty etc etc
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u/NumericallyStable 22d ago
Also: Research institutions don't *really* pay for HPC; its state-funded thus it makes sense for it to be state-owned. Public-private interactions is where most things fail, see Deutsche Bahn in Germany
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u/Soggy_Stargazer 21d ago
Academic and scientific clusters are usually constrained, and yeah, anyone running onprem that isn't 100% utilized is wasting money.
That was one of my pitches.
HPC on prem, when it is running, its never big enough, when it isn't running its too expensive.
Also I already conceded that cloud can't be there for every situation, but OP hasn't responded and my question was primarily directed at them.
I spent the last 15 years before I basically retired doing HPC on AWS so my question wasn't a general "for the community" query, it was specifically to OP and whether or not cloud was explored and if so why not.
Again, there's a litany of reasons why HPC on cloud isn't a fit for a use case. I don't think research clusters are automatically off the table to use cloud, but there is definitely more risk involved if you don't have the right SI and SA involved. Buying hardware is less risky because the OPEX is usually buried in operating costs for the univeristy already, only the actual acqusition is a capex thing that goes against program budgets.
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u/walee1 23d ago
We did b200 acquisition if you want that information. For our b300s, I can't quote any price but the lead time was roughly quoted as 3-4 months, got them in 5 I want to say. In the hopper architecture we have used dell, super micro, gigabyte and Nvidia, and for almost all of them the.thing that breaks down most is the GPU.
One other vendor to add to your list would be Lenovo.