Machine Learning GPUs: On Your Premises or in the Cloud?

By noasa

5.22.2024 twitter linkedin facebook

Twenty years ago it would be nearly unheard of for a business to collect and manage petabytes of data, but this is the reality for most companies today. The volume is massive, but with the right analytics, this data can tell stories about customer behavior, preferences, habits, demographics, and much more. 

With today’s machine learning (ML) capabilities,) it’s possible to formulate more complex queries and derive deeper, more accurate analytics and predictions from massive data sets. In fact, the more data there is, the higher quality the analysis can be. With big data, ML can more effectively provide insights, identify correlations, detect anomalies, and more.  

For this to work properly, large datasets need to be processed and complex algorithms applied efficiently and at incredible speeds. That is precisely what graphics processing units (GPUs) were designed to do, which is why they’ve  become increasingly common for many AI-driven solutions. 

GPUs for Big Data Analytics and Machine Learning

Initially developed to improve the gaming experience, GPUs were designed with parallel processing architecture to simultaneously carry out many complex graphics-related calculations. This makes them far faster than the central processing unit (CPU), the brain of the computer, which GPUs were initially introduced to supplement. But the GPU was a leap in computing power that would not remain limited to graphics for long.  

GPUs excel at handling many repetitive small computations, so data-intensive and complex workloads can be divided into smaller tasks for high-speed processing. Fewer GPUs are needed for the same task when carried out by CPUs, with their linear, step-by-step processing. GPUs are also more energy-efficient than CPU arrays. 

This is why GPUs initially turned up at the forefront of cryptocurrency mining. Eventually, their capabilities were harnessed for big data analytics, real-time analytics, ML at scale, and other tasks leveraging large, diverse datasets. GPUs are now being embraced in industries such as finance, healthcare, earth sciences, architecture, cybersecurity, and many others. 


GPUs accelerate simulations, which are particularly common use cases in scientific fields. Generating accurate simulations of real-world phenomena and trends requires processing large quantities of historical data. GPUs allow for more comprehensive data to be quickly integrated and analyzed, which means more effective simulations. 

Risk modeling

GPUs can be used to develop statistical models for assessing risk based on the analysis of millions of variables and their mutual interactions. In addition, the speed with which GPUs accomplish these assessments allows organizations to react in real time to changing circumstances as needed. More data and faster analysis mean more reliable forward-looking models.

Deep learning

Deep learning – training computers to process information by emulating human neural processes – requires rapid, parallel computational processes. The latest GPUs on the market have been designed for deep learning and the major deep learning Python libraries already support the use of GPUs.

Predictive AI

GPUs are used in training sophisticated ML models for predictive analytics, identifying patterns in past events, and making predictions about the future. This is due to the GPUs specific processing strengths in applying linear regression algorithms for supervised learning. 

Cloud-based or On-Premises: Pros and Cons

While GPUs significantly boost performance compared to CPU-only solutions, enterprises still need to take into account the ever-increasing costs of machine learning dependent on expanding raw datasets. Many organizations are therefore looking into how they can get the fastest results from the GPUs in their data stack infrastructure at the most reasonable cost.  

One key element is deployment. Should they invest in on-premises GPU setups or leverage cloud-based GPU solutions? 

To answer that question, it’s necessary to consider requirements like specialized hardware, high-volume storage, performance continuity, and more. 

On-Premises GPU

A dedicated on-premises GPU server for machine learning purposes is dependent, first and foremost, on in-house capabilities and infrastructure. 


Full control over the hardware, the GPUs, remains in the company’s hands at all times. That means the company can easily and freely customize its infrastructure, the related data storage, software and processes as it sees fit in a more secure and cost-effective way.

For dedicated tasks requiring high memory bandwidth and low latency, such as deep learning, on-premises GPUs may provide better performance. This may even be a necessity if the organization requires a cutting-edge technology that is not yet available from any cloud-based service provider. 

The on-premises hardware needs to be purchased, which is a clearly defined one-time cost. This is in contrast to ongoing, sometimes unpredictable fees (renewable subscriptions, pay-per-use, etc.) required for the use of cloud-based GPU arrays. 


The flip side of clarity in a one-time purchase of on-premises GPU hardware is that it can be a relatively high upfront investment. Depending on a company’s available resources and immediate need, this can be a significant consideration. 

An on-premises GPU solution requires a physical space for the hardware. This includes cooling infrastructure to ensure proper long-term functioning of the system. Similarly, regular maintenance and upgrades to the infrastructure, hardware, system, databases, and related on-premises applications are the company’s responsibility. This constitutes additional ongoing costs, which include the need to maintain in-house IT expertise related to the GPU cluster. 

Scaling to meet changing needs with an on-premises solution requires purchasing more servers and investing the effort required to integrate them. In the cloud, in contrast, it is easier to set up another machine and most data platforms even auto-scale in response to volume-based triggers. 


An important task that on-premises GPUs are generally a good fit for is ML model training, a critical step to accurate AI-driven predictive analytics. SQream’s in-database model training feature takes advantage of GPU acceleration to train models directly within the database, maximizing efficiency and enhancing big data analytics.

GPU in the Cloud

Cloud-based GPUs require no locally maintained servers or other hardware. They are essentially remote-access data centers for machine learning, providing fast access to high performance computing power that can exceed that of on-premises GPU clusters.


Cloud GPUs do not require a large initial outlay, keeping upfront costs down. The pay-as-you-go or subscription models for use of cloud-based GPUs ensure that companies only pay for what they actually use, with no traditional flat-fee licensing costs.  Notably, there are already even some GPU-specialized cloud providers like Vultr or Lambda Labs.

With infrastructure for GPU-based machine learning in the cloud, companies have maximum flexibility in terms of adjusting their use of resources as needed. They can easily and quickly scale up or down to meet fluctuating demand or budget restrictions without any back-end costs whatsoever.

There is no need for physical space, nor are there any maintenance or regular upgrade costs for cloud-based GPUs. Similarly, cloud-GPU-based machine learning and associated data sets can be accessed from anywhere, across different platforms, on demand.


Keeping recurrent costs down can be a challenge when using cloud GPUs, especially for intense machine learning tasks. Actual usage may not be completely clear until the bill arrives, with possible surprises due to peak times, data spikes, or other unforeseen business-specific developments. In addition, cloud platforms and services may use multilayered pricing that breaks down differently depending on the region. 

Latency is dependent on internet connectivity when using cloud resources. Any significant interruption or slow-down will impact cloud services, which can have a highly disruptive impact on complex GPU-based machine learning processes. 

The type, capabilities, and number of GPU machines is limited to what the cloud provider offers. This may not meet a company’s specific need for a customized server, for example, with specific RAM, number of cores, or other parameters.

Data security, privacy and regulatory compliance are also in the purview of the cloud GPU service provider. This becomes a concern when sensitive company information is stored remotely for machine learning purposes, separately from other core systems. Even when the standard security measures of the cloud provider are a relative improvement over local capabilities, the service agreement generally places the burden of securing sensitive data stored in the cloud on the user.


The shift to GPU-accelerated data analysis generally brings about a dramatic change in machine learning capabilities and improves decision-making. The time, cost, and personnel needed to set up an on-premises GPU architecture, however, may not be justified for some companies. SQream Blue makes it easy to start benefiting from GPU-powered cloud solutions without the need to set up the infrastructure. 

Choosing the Right Option

The choice between on-premises GPUs and cloud GPUs is not black and white, as each option has its pros and cons. And every company will weigh the costs and benefits differently, in accordance with their own specific business needs, budget constraints, data sensitivity, and scalability requirements.

Key benefits of cloud GPUs: 

  • A low initial financial barrier
  • Support from cloud service providers
  • No maintenance or upgrade costs
  • Rapid, seamless scalability

Key benefits of on-premises GPUs: 

  • Unlimited use 
  • High bandwidth, consistent low latency
  • Sensitive data secured locally
  • Greater customizability

In fact, perhaps a definitive answer to the question of on-prem or cloud GPUs is not immediately necessary. Starting with a cloud solution can provide the necessary feedback, validating the need for GPU-accelerated machine learning with a minimal upfront investment of time, resources, and money. If and when the experiment is successful, and the impact is felt in the company’s bottom line, they may decide to make more significant investments in on-premises GPU-based hardware. The ROI will be felt over time, as will the increased flexibility in terms of customization to meet proprietary needs.

Regardless of how a company chooses to proceed, using GPUs for machine learning and big data analytics can significantly enhance computational capacity and insight generation.