If you're a university researcher working with AI, machine learning, or high-performance computing (HPC), you’ve probably felt the frustration of waiting for GPU access. Whether you're training AI models, running simulations, or processing massive datasets, GPUs are the backbone of modern research.

Why researchers rely on GPUs.

From AI breakthroughs to scientific discoveries, GPUs power some of the most advanced research across disciplines:

• AI & Machine Learning – Training large language models, computer vision applications, NLP, and generative AI.
  
  
• Big Data & Data Science – Analyzing massive datasets in healthcare, climate science, economics, and finance.
  
  
• Physics, Engineering & Computational Research – Running fluid dynamics, structural analysis, astrophysics, and material simulations.
  
  
• Bioinformatics & Genomics – Accelerating DNA sequencing, molecular modeling, and drug discovery.
  
  
• Rendering, Visualization & Digital Media – Powering 3D rendering, medical imaging, and scientific visualization.
  
  
• Cybersecurity & Cryptography – Running encryption algorithms, penetration testing, and AI-driven threat detection.
  
  

While many universities have invested heavily in purchasing GPUs, many university researchers face a frustrating reality: they simply can’t get access to the computing power they need.

What’s the problem?

In speaking with researchers and university IT admins, the same challenges come up again and again:

GPUs are buried in workloads.

University GPU clusters are a shared resource, and demand has exploded. Faculty, graduate students, undergrads, and entire departments are competing for the same limited compute power. Queues can stretch for days or even weeks, delaying projects and forcing researchers to work around slow, inefficient schedules.

Adding GPUs is pricey.

Universities can’t just buy more GPUs whenever demand increases. Expanding on-premise compute infrastructure is expensive—it requires not just the hardware itself, but ongoing investments in IT support, maintenance, cooling, and power. This means that even as research teams grow and AI workloads become more compute-intensive, GPU capacity remains stagnant or falls behind.

Newest models are hard to come by.

While industry leaders are training AI models on H100s and preparing for Blackwell GPUs, many university clusters are still running on older-generation hardware that’s years behind. This leads to:

• Slower model training – What takes a few hours on modern GPUs could take days on older ones.
  
  
• Reduced efficiency – Researchers waste time on optimizations just to get outdated hardware to perform.
  
  
• Incompatibility issues – New AI frameworks and models are designed for newer GPUs, leaving researchers stuck troubleshooting compatibility problems.

Not all projects are considered equal.

Most universities use job scheduling systems like Slurm, where researchers must submit jobs and wait their turn. Faculty-led projects often get priority over student research, and independent or exploratory work may be pushed to the back of the queue. If you’re not part of a well-funded lab, your access can be severely limited.

Not enough variety available.

Different workloads require different types of GPUs—but universities rarely offer variety. Whether you need high-memory GPUs for large models, A40s for visualization, or multiple GPUs for parallel processing, you’re often stuck with whatever is available. This forces researchers to use suboptimal hardware, leading to inefficiencies and workarounds.

Limited off-campus access.

University clusters are usually tied to the campus network, making it difficult for researchers to work remotely or collaborate with external teams. If you’re traveling, doing field research, or working with an international team, on-prem GPUs are often inaccessible.

The high cost of GPU shortages.

The GPU shortage at universities isn’t just an inconvenience—it actively holds back research, delays discoveries, and hurts both researchers and institutions.

For principal investigators (PIs):

• Funding & grant deadlines at risk – Research proposals and grant-funded projects often have strict timelines. If GPU shortages delay progress, PIs risk missing deadlines, losing funding, or failing to meet the deliverables promised in their grants.
  

• Competitive disadvantage – In AI research, speed matters. If a lab can’t access compute resources, it falls behind competitors at well-funded institutions with better infrastructure.
  

• Pressure to secure external compute resources – Many PIs end up spending valuable time trying to secure external GPUs, whether through cloud services, collaborations, or industry partnerships, instead of focusing on research.

For researchers & students:

• Delayed research & missed publication deadlines – Many conferences and journals have strict submission deadlines. If researchers can’t access GPUs in time, they risk missing key opportunities to publish their work.
  

• Wasted time on inefficient workarounds – Instead of focusing on research, students and researchers waste hours optimizing code, troubleshooting slow hardware, and resubmitting jobs to overloaded queues.
  

• Limited hands-on learning for AI/ML students – Coursework and research projects often require GPU compute, but when resources are scarce, students get less hands-on experience, making them less competitive in the job market.

For universities:

• Difficulty attracting top research talent – Top-tier researchers want access to cutting-edge resources. If a university can’t provide adequate compute, it risks losing talent to institutions with better infrastructure.
  

• Lower research output & reputation impact – Delayed or canceled projects mean fewer high-impact papers and less recognition for the university. This can hurt rankings, funding, and partnerships.
  

How on-demand GPUs eliminate wait times.

on-demand GPUs offer an alternative that eliminates the frustrations of on-prem clusters. Here’s how they transform access to high-performance compute:

No more waiting in line.

on-demand GPUs provide on-demand access, meaning researchers can launch a job immediately instead of waiting in a queue for days or weeks. This speeds up project timelines and boosts productivity.

Scale up or down instantly.

With on-demand GPUs, researchers can scale their compute resources as needed. Whether you're running a quick test or a massive training job, you’re not limited by fixed on-prem capacity.

Access the latest hardware without the cost.

Cloud providers regularly update their GPU offerings, meaning researchers can access the latest models—like NVIDIA H100s—without their university having to invest millions in new infrastructure.

More cost-effective than buying GPUs.

By utilizing cloud resources, universities can reduce the financial burden of purchasing and maintaining expensive hardware. Researchers pay for the compute power they use, optimizing budget allocation and reducing idle resource costs.​

Work from anywhere, collaborate globally.

Cloud-based platforms enable seamless collaboration among researchers worldwide, facilitating data sharing, joint analyses, and collective problem-solving without geographical constraints.

The future of research is flexible compute.

For universities struggling to keep up with GPU demand, cloud compute offers a scalable, cost-effective solution. Whether you’re training AI models, processing massive datasets, or running scientific simulations, on-demand GPUs eliminate wait times and unlock new research possibilities.

Want to see how on-demand GPUs can help your research? Shop our wide inventory of available GPUs today.