Home > > > How Does Using a Public Cloud Computing Service Compare to Having Your Own Private Cluster/Supercomp

How Does Using a Public Cloud Computing Service Compare to Having Your Own Private Cluster/Supercomp

Author : diwofi lio | Published On : 18 Apr 2025

In the realm of high-performance computing (HPC), one of the most critical factors determining the success of a project is speed—how quickly computations are completed, data is processed, and results are returned. With the rise of cloud computing, businesses, researchers, and developers are faced with an important choice: should they rely on public cloud computing services, or invest in building and maintaining a private cluster or supercomputing facility?

This article explores the performance differences between public cloud services and private computing infrastructure, with a particular focus on speed, while also touching on the underlying factors that influence these performance outcomes.

Understanding the Landscape: Public Cloud vs. Private Infrastructure

Before diving into speed comparisons, it's important to understand what each option entails.

Public Cloud Computing refers to services provided by third-party vendors such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). These providers offer scalable computing resources on-demand, typically accessed over the internet. Users can spin up thousands of virtual machines, GPUs, or containerized environments within minutes.

Private Clusters/Supercomputers are dedicated computing systems owned and operated by a single organization. These could range from a modest on-premises cluster of servers to a full-fledged supercomputing facility with specialized hardware and network architectures. The infrastructure is custom-built and tailored to the organization's specific computational needs.

Raw Speed and Performance

Speed in computing can be broken down into multiple dimensions: processing power (CPU/GPU speed), memory speed, storage I/O, and network bandwidth/latency. Here's how the two computing paradigms stack up:

1. Processing Power (CPU and GPU Performance)

  • Private Supercomputers are often optimized with cutting-edge CPUs and GPUs tailored for specific workloads (e.g., scientific simulations, AI training). Organizations can configure them for peak performance without the constraints of virtualization or resource sharing.

  • Public Cloud Providers offer access to a wide range of hardware, including high-performance instances (e.g., AWS EC2 C6i or Nvidia A100 GPUs). While these can be extremely fast, they are typically virtualized and multi-tenant, introducing some overhead and potential for performance variability due to "noisy neighbors."

Verdict: For consistently high, deterministic CPU/GPU performance, private infrastructure may edge out the cloud. However, cloud providers are closing the gap rapidly.

2. Memory and Storage Speed

  • Private Clusters can be fine-tuned with the latest RAM technologies and ultra-fast NVMe or SSD storage, giving predictable low-latency performance. Data locality can be optimized since storage and compute often reside within the same physical network.

  • Cloud Environments usually offer high-performance storage solutions, but they are network-attached and shared. Latency can be higher, especially if storage is remote (e.g., object storage like Amazon S3). Memory access speeds may be limited by the virtualization layer.

Verdict: Private systems tend to provide faster, more predictable memory and I/O speeds. Cloud can compete but may require more configuration and incur higher costs.

3. Network Performance

  • In Private Systems, organizations can use high-speed interconnects such as InfiniBand or 100GbE, which are critical for parallel processing and tightly coupled workloads.

  • Cloud Networks are generally fast, but the virtual nature introduces overhead. While premium options like AWS's Elastic Fabric Adapter can help, latency and jitter may still be higher compared to tightly integrated on-premises clusters.

Verdict: For low-latency, high-bandwidth inter-node communication, private systems (especially with InfiniBand) outperform standard cloud setups.

Elasticity and On-Demand Scaling

One of the cloud's strongest advantages is elasticity. Need 10,000 CPUs for a few hours? The cloud can deliver that almost instantly. Private systems, no matter how fast, are limited by available hardware. Scaling up means capital investment and lead time for procurement and setup.

However, there's a tradeoff—cloud speed may vary depending on resource availability, and spinning up resources during peak demand times can result in slower provisioning or throttling.

Verdict: For bursty workloads that need rapid scaling, cloud can provide "speed of access" that private systems can't match, though raw compute speed may be slightly lower.

Workload Suitability

The type of workload heavily influences which system will be faster.

  • High-Throughput, Loosely Coupled Workloads (e.g., rendering, simulations with low inter-process communication): These are often well-suited to cloud environments. Public cloud can offer thousands of parallel tasks with minimal performance penalties.

  • Tightly Coupled HPC Workloads (e.g., molecular dynamics, climate modeling): These benefit from ultra-low latency and high bandwidth interconnects. Private supercomputers often excel in this area.

  • AI and ML Training: Both cloud and private systems can be competitive here. The cloud offers access to cutting-edge GPUs and TPUs without the need for upfront investment, but private systems can be optimized for specific frameworks for better performance.

Performance Variability

A key distinction is performance consistency.

  • Private Systems offer consistent performance because resources are dedicated. This is crucial for benchmarking, repeatable scientific experiments, or real-time applications.

  • Public Cloud instances can suffer from variability. Even with dedicated instances, background processes and shared infrastructure can introduce noise. This can make fine-tuned performance benchmarking difficult.

Verdict: For mission-critical tasks where predictability is key, private systems are often preferred.

Cost-Speed Tradeoff

Speed is also about cost-efficiency. How much are you willing to pay for faster results?

  • In the cloud, you can throw massive resources at a problem for quick turnaround—but this can be expensive. It's often ideal for short-term, high-intensity tasks.

  • Private Infrastructure has high upfront costs but lower marginal cost per computation over time, assuming high utilization.

Verdict: If speed must be maximized regardless of cost, cloud might win. For long-term, sustained speed at lower cost, private systems may offer better ROI.

Hybrid Approaches: The Best of Both Worlds?

Many organizations now use hybrid models, combining private clusters for steady-state workloads and cloud bursts for peak demand. This approach allows them to maintain high speed and consistency, while leveraging the cloud's scalability when needed.

Conclusion

So, which is faster—public cloud or private supercomputing?

The answer depends on your workload, performance requirements, and budget. In general:

  • Private clusters offer faster, more consistent raw performance, especially for tightly coupled workloads.

  • Public cloud provides rapid scalability and can match or even exceed private systems in some scenarios, particularly for embarrassingly parallel tasks or AI/ML jobs.

Ultimately, it’s not just about speed in isolation—it's about matching the right infrastructure to the right workload. For many organizations, combining both worlds through a hybrid strategy is proving to be the fastest path forward.