Cloud v. On Prem HPC: A Strong Comparison Metric
When enterprises evaluate on-prem high-performance computing (HPC) platforms, with an eye toward benchmarking against cloud, the conversation often starts with hardware specifications or infrastructure pricing—but that view is incomplete. Smart organizations increasingly assess HPC investments with a view toward workload, focusing on measurable outcomes such as cost per job, cost per simulation, time to results, and overall throughput. So, instead of asking, “What does this cluster cost?” they ask, “What does it cost to complete a simulation, train a model, or run an analysis?”
This shift reframes procurement decisions around productivity, scalability, and return on investment, ensuring that platform selection aligns directly with business objectives, engineering timelines, and research impact rather than infrastructure expense alone. The fact is, simulation runtime is one of the largest drivers of HPC cost efficiency. Given this, decision makers should be evaluating the following factors:
- Average simulation runtime across infrastructure options: Buyers should compare how long
representative workloads take to complete on each platform. Even modest reductions in runtime can significantly lower cost per simulation, improve engineering throughput, and accelerate innovation cycles.
- Resource utilization efficiency during workload execution: High core counts or powerful GPUs mean little if they are underutilized. Evaluating CPU, memory, and accelerator utilization during real-world workloads ensures the infrastructure is architected to deliver maximum computational output per dollar spent.
- Storage and I/O performance impact: Storage architecture can materially influence runtime, especially for data-intensive simulations and AI workloads. Insufficient I/O throughput, high latency, or poorly configured storage tiers can create bottlenecks that negate gains from fast processors or GPUs.
- Queue scheduling efficiency: Inefficient job scheduling leads to idle hardware, longer wait times, and reduced overall system productivity. Enterprises should assess how effectively a platform manages workload prioritization, node allocation, and concurrency to maintain consistent throughput.
- Data transfer overhead costs: In cloud or hybrid environments, moving large datasets between storage and compute nodes—or between on-prem and cloud—can introduce both financial costs and performance delays. Decision makers must quantify how data locality and transfer frequency impact total cost per job.
- GPU scaling efficiency: Many modern HPC and AI workloads rely on multi-GPU scaling. However, performance gains are not always linear. Buyers should evaluate how well applications scale across multiple GPUs and nodes to ensure incremental hardware investment translates into measurable performance improvement.
- Application optimization support: Infrastructure alone does not guarantee performance. Vendor expertise in workload tuning, compiler optimization, parallelization strategies, and application-specific benchmarking can significantly reduce runtime and increase overall return on investment.
- Business Impact of Faster Simulation Cycles: Reduced simulation runtime accelerates product development cycles.
Ultimately, the most meaningful HPC comparison is not cloud versus on-prem in isolation, but which environment delivers the lowest cost per result and the fastest path to insight. When enterprises evaluate platforms based on runtime efficiency, workload scalability, and real-world throughput, on-prem HPC often demonstrates measurable advantages in performance control, predictability, and long-term value. By aligning infrastructure decisions with application behavior and business impact, organizations position themselves to reduce cost per simulation, accelerate engineering timelines, and gain a sustained competitive edge. To get started or schedule a no-cost, in-depth consultation, call 952-808-1000, email engineering@nor-tech.com or visit https://www.nor-tech.com.
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Nor-Tech is on CRN’s list of the top 40 Data Center Infrastructure Providers along with IBM, Oracle, Dell, and Supermicro and is also a member of Hyperion Research’s prestigious HPC Technical Computing Advisory Panel. The company is a complete high performance computer solution provider for 2015 and 2017 Nobel Physics Award-contending/winning projects. Nor-Tech engineers average 20+ years of experience. This strong industry reputation and deep partner relationships also enable the company to be a leading supplier of cost-effective Lenovo desktops, laptops, tablets and Chromebooks to schools and enterprises. All of Nor-Tech’s high-performance technology is developed by Nor-Tech in Minnesota and supported by Nor-Tech around the world. The company is headquartered in Burnsville, Minn. just outside of Minneapolis. Nor-Tech holds the following contracts: Minnesota State IT, University of Wisconsin System, and NASA SEWP V. To contact Nor-Tech call 952-808-1000 or visit https://www.nor-tech.com.