In the AI era, universities and institutes are focused on providing students with the skills and resources needed to develop advanced AI models, including large language models (LLMs), computer vision models, and multimodal AI systems.
Traditionally, educational and research institutions invested in physical hardware maintain on-premises servers and GPUs for student use. This approach, however, presents several challenges, such as high upfront costs, ongoing maintenance, and limited scalability.
In this article, we will explore these challenges and explain why cloud GPUs offer a far more efficient and scalable alternative for higher education.
Why Do Universities Need Advanced GPUs for AI Education
Training AI models, or creating workflows with them — especially large language models (LLMs), computer vision systems, automatic speech recognition (ASR), text-to-speech (TTS), and multimodal AI — requires immense computational power. The process of training or fine-tuning these models involves handling vast datasets, executing complex algorithms, and managing high-dimensional data, all of which place significant demands on hardware. As you might know, traditional CPUs fall short when it comes to these tasks.
For instance, when working with models like Llama 3.1, Mistral variants, Pixtral-12B, or advanced computer vision models such as the YOLO series, the sheer scale of computation needed is beyond what CPUs can handle efficiently. Without access to high-performance GPUs, both the training and inference phases of these models can take exponentially longer, stalling progress and limiting experimentation.
To work with real-world data at scale, students must have access to GPUs. Whether they are fine-tuning large-scale LLMs, building Retrieval-Augmented Generation (RAG) systems using vector stores and knowledge graphs, or developing object detection models for image classification, GPUs are essential. Without them, students wouldn’t be able to proceed with the actual development of these advanced AI models.
Providing access to advanced GPUs, such as the HGX H100 or A100, empowers students to push the boundaries of innovation. These high-performance GPUs are specifically built to support the intensive demands of deep learning frameworks, allowing students to experiment with cutting-edge AI technologies and prepare for real-world AI challenges.
Challenges of On-Premise GPU Infrastructure
While on-premise GPU infrastructure may seem like a straightforward solution for providing students with the hardware needed to learn and experiment with AI, it comes with several significant challenges that can hinder both educational outcomes and long-term sustainability.
High Initial Costs
The upfront cost of purchasing state-of-the-art GPUs is substantial. Modern GPUs like the HGX H100 or A100, which are essential for advanced AI workloads, come with a hefty price tag. In addition to the GPUs themselves, institutions must invest in the necessary infrastructure—servers, networking equipment, and storage systems—to support them. This creates a significant financial barrier. Additionally, the need for multiple GPUs to support a larger student base can quickly escalate costs.
Resource Allocation
With limited on-premise resources, universities often face challenges in allocating GPU access effectively among students. As the number of students requiring GPU access grows, the available hardware can become a bottleneck. This leads to scheduling conflicts, where students may have to wait for access to GPUs, stalling their work. In a competitive learning environment, such delays can impede students’ ability to complete assignments or research promptly.
Obsolescence
The pace of advancement in GPU technology is rapid, and on-premise hardware quickly becomes outdated. What may be considered cutting-edge today could be obsolete within a few years. The financial burden of constantly upgrading to the latest GPUs is a major challenge for universities, especially when older equipment may no longer support the latest AI frameworks and techniques. This obsolescence creates a cycle of frequent hardware replacements, increasing costs.
Maintenance and Upkeep
Once the hardware is in place, ongoing operational expenses present another challenge. On-premise GPU infrastructure requires continuous maintenance, from hardware monitoring to ensuring optimal performance. Running high-performance GPUs also leads to increased electricity consumption and cooling requirements, significantly raising operational costs. Physical maintenance, such as replacing faulty components or upgrading infrastructure, adds another layer of complexity and expense that universities must account for.
Scalability Issues
On-premise infrastructure is often rigid and difficult to scale. As new advancements in GPU technology emerge or as student demand for resources grows, scaling up on-premise systems can be cumbersome and expensive. Universities might find themselves needing to purchase additional GPUs or servers, which not only incurs more costs but also requires physical space and additional infrastructure upgrades.
Why Cloud GPUs are a Superior Alternative
In many ways, cloud GPUs offer a more flexible, cost-effective, and scalable solution compared to on-premise infrastructure. Leveraging cloud-based GPUs allows you to bypass many of the challenges that come with maintaining physical hardware while providing students with the cutting-edge resources they need to succeed.
Let us list a few of these reasons below.
Cost Efficiency
The immediate benefit of using cloud GPUs is the elimination of large upfront investments. Instead of spending significant amounts of capital on purchasing state-of-the-art GPUs and the accompanying infrastructure, you can provide students access to high-performance cloud GPUs on-demand without the need for long-term financial commitments.
Additionally, cloud service providers (CSPs) take care of ongoing maintenance. This means that you have essentially outsourced the infrastructure upkeep headache to a provider.
Access to the Latest GPU Technology
This is one big advantage of using AI-focussed cloud providers. With cloud GPUs, you always have access to the latest GPUs, including cutting-edge models like the HGX H100 and A100. In a field where GPU technology is evolving quickly, this ensures that your students and researchers are working with the latest and greatest. This enables them to gain hands-on experience with the newest GPU architectures, which are designed to handle the advanced demands of AI workloads such as large-scale language models, computer vision tasks, and multimodal AI applications.
Flexibility and Scalability
One of the biggest advantages of cloud GPUs is their inherent flexibility. Cloud infrastructure can be scaled on demand, allowing you to adjust GPU access based on class sizes, specific projects, or research needs. This scalability means you can easily ramp up resources during peak periods, such as final projects or large research initiatives, and scale back during quieter times. This dynamic flexibility enables institutions to accommodate fluctuating needs without overcommitting to expensive hardware.
Improved Accessibility
Cloud GPUs allow students to access resources from anywhere, providing a level of accessibility that on-premise infrastructure simply cannot match. This is especially important in today’s increasingly remote learning environment, where students may be collaborating across different locations or working on projects outside of traditional classroom settings. With cloud-based access, students can easily tap into the GPU resources they need, enabling them to collaborate on AI projects in real time, regardless of their physical location.
Pay-As-You-Go Model
The cloud’s pay-as-you-go model is particularly beneficial for educational institutions, where budgets are often tight and resource needs vary. Instead of paying for unused capacity or overcommitting to hardware that sits idle, you only pay for the resources you use. This allows for optimized cost efficiency, as you can align your expenses directly with the specific needs of your students and faculty. Whether scaling up for a large research initiative or providing GPUs for a small course, the pay-as-you-go model offers the financial flexibility to adapt without waste.
Impact on Research and Innovation
One fundamental way in which cloud GPUs impact higher education institutions is their impact on research. For students and faculty working on cutting-edge AI research, the ability to rapidly iterate on complex models, process massive datasets, and test novel hypotheses at scale is critical. Cloud GPUs enable this by providing unparalleled computational power and flexibility, empowering researchers to push the boundaries of AI and machine learning.
For instance, consider the task of training a large language model (LLM) on Indic languages. This is a highly iterative process — it involves refining models, tuning hyperparameters, and experimenting with different architectures. These tasks are computationally intensive, especially when dealing with transform architecture models. Cloud GPUs address this bottleneck by offering scalable resources on demand. Researchers can quickly scale up GPU power to shorten training times.
Additionally, the ability to integrate cloud-based storage solutions with cloud GPUs ensures that researchers can seamlessly access and process large datasets without the limitations of local storage. This makes it easier to collaborate on large-scale projects, where data is constantly being updated.
Generative AI architectures are also driving cutting-edge breakthroughs in fields like drug discovery and protein sequencing, where AI models are used to predict molecular structures, simulate chemical reactions, and generate novel compounds for therapeutic use. In drug discovery, generative models can help design and evaluate potential drug candidates. Similarly, in protein sequencing, AI models are revolutionizing the way researchers predict protein folding and design new proteins with desired properties. These tasks require immense computational power due to the complexity and high dimensionality of the data involved. Cloud GPUs, particularly in large-scale clusters like 64xH100 or 256xH100, provide the necessary computational muscle to handle these tasks efficiently.
Cost Analysis
When deciding between cloud-based GPU and on-premises GPU, let’s understand the actual costs involved. To understand this fully, reach out to our team for a discussion. Below, we have created an indicative comparison.
For on-premises GPUs, the initial investment is significant. For example, an H100 GPU costs around INR 3,092,383 to purchase upfront. Similarly, an A100 - 40GB GPU costs about INR 814,291. These prices may be a significant commitment for educational institutions since they need to install not only the hardware but also need to set up the necessary infrastructure, including cooling systems.
Also, keep in mind that newer GPUs like H200 are on the horizon. GPU technology is one of the fastest evolving technologies in the AI domain currently, and a current generation GPU may become obsolete in less than a year.
On the other hand, cloud-based GPUs have a much lower barrier to entry. Instead of spending huge amounts upfront, institutions can get access to the same powerful GPUs through cloud providers at an hourly rate, which may cost a fraction per hour.
For instance, provisioning an 8xH100 GPU in the E2E cloud costs approximately INR 2800 per hour, while a single A100 - 40GB is available for INR 170 per hour.
*Incl. 200,000 approx. annual maintenance
# For 8 hours/day usage for 365 days
*Incl. 100,000 approx. annual maintenance
# For 8 hours/day usage for 365 days
Therefore, with cloud computing, institutions do not have to pay a hefty upfront investment and they gain the flexibility to adjust GPU usage as needed. It should also be noted that the calculation has been made for 8 hours per day throughout the year. However, institutions may have holidays throughout the year, thereby having a lot of days without any computational requirement. This means that the actual cost of cloud GPU would be far less. With committed nodes, the cost goes down much further.
Security and Compliance
Another major reason for adopting cloud-based GPUs is security. Cloud service providers like E2E Cloud are MeitY-empaneled and NSE-listed. The infrastructure has been designed to adhere to stringent IT laws of the country.
Compliance is yet another major reason. Institutions have to follow the strict regulations of their countries, and this requires resources and expertise to implement. With a cloud provider like E2E Cloud, the data stays within Indian borders. This means that institutions get the benefits that come with an India-born cloud, with highly competitive rates, and quality that surpasses other hyperscalers.
Future Outlook
AI is advancing rapidly and hence technologies like AI, machine learning, and big data will continue to evolve. Staying cutting-edge means access to the best models and high-end compute resources. This means that higher education institutions that are still using on-premise GPUs are likely to shift to cloud-based GPUs in the future so that they can stay at the forefront of AI research. This would enable them to gain access to the latest GPUs for building AI solutions.
E2E Networks is the first hyperscaler to bring the latest GPUs to India. This means that educational institutions would have access to the most cutting-edge GPUs whenever new GPU models are released. They do not need to get committed to a single machine or type of hardware.
Final Thoughts
Transitioning to cloud-based GPU access cannot be considered as just as a technical upgrade; it is also a catalyst for innovation in higher education. Cloud-based GPUs have lots of advantages over on-premise GPUs including scalability, cost-effectiveness, flexibility, accessibility, and better collaboration. By adopting cloud-based GPUs, higher educational institutions can improve their research capabilities and ensure that their students have the knowhow of the latest AI.
Ready to power your AI programs with cloud GPUs? Take the next step by partnering with E2E Networks to unlock advanced cloud-based GPU infrastructure that can boost your research capabilities and drive better student outcomes. Reach out today to explore how we can help elevate your AI initiatives to the next level.
