Renting GPU for Scalable and Cost-effective AI Development

Why is Renting GPU for AI Projects a Better Option?

Developing an AI solution involves numerous tasks with parallel processing requirements from handling complex algorithms to training and fine-tuning models. For Vision AI projects and Gen AI projects that require large-scale fine-tuning of LLM models, massive text-image-video-based data handling and real-time streaming require immense computational power, speedy processing, and storage space. This means an AI project cannot be completed without utilizing GPUs. Our experience with several AI projects has taught us that renting GPUs is better than buying them. Here are some of the benefits of Renting GPU for AI development:

Lower Entry Barrier: The high upfront investment in GPU often stops small businesses or startups from building AI-based solutions. A NVIDIA GPU costs approximately between USD 700 and 40,000, depending on the model and specifications. Whereas, you can rent a GPU for approximately between USD 0.23 and 9.40 per hour (depending on the brand and model) on cloud rental services like Vast.ai. So, utilizing a rental GPU for AI development provides a low entry barrier.

Access to Latest Technology: Any electronics enthusiast knows that a better version is always around the corner. And with evolving technology like GPU, there’s always something new coming to improve efficiency and productivity in AI development. So, why own a GPU and see it getting outdated rather than rent the latest one to complete your project?

Scalability: Many cloud-based GPU rental services offer flexible pricing models with hourly, daily, or monthly rates. They also offer pay-as-you-go models so you can manage costs for your limited-budget projects by upscaling and downscaling GPU usage as your requirements change.

Low-cost Experimentation: AI development is incomplete without a fair share of experimentation with different models and configurations, especially when you build custom solutions. With low rentals, you can afford to develop multiple test models for exploration and innovation, and achieve breakthroughs in tailoring solutions for your niche requirements.

Reduced Operational Costs: Expenses associated with buying a GPU like hardware maintenance, timely upgrades, energy consumption, hardware failures, etc., do not apply when renting a GPU. It’s because the GPU rental services take care of these critical matters. So, once your rental bills are sorted as per your budget, you can focus on innovation rather than managing hardware resources.

Risk Mitigation: By renting rather than making a long-term investment in buying a GPU, you keep your project commitments flexible to adapt to changing market conditions. If your project requirements change over time, you can simply rent a different GPU to meet them.

Supplementing Your GPUs: Companies who own GPUs often find them failing to meet the requirements of advanced AI development, thanks to continuous innovation. They can combine the capabilities of their owned GPUs with rented ones to complete specific tasks.

Faster Time to Market: When you need more rapid deployment of AI solutions, you can rent additional GPUs and accelerate model training and development. If your time-bound project demands greater efficiency and agility, renting GPUs is not only a smart but a strategic move.

Leveraging the Latest Trend: Even though the concept of renting GPUs is only three to four years old, several service providers like Hyperstack Cloud have already entered the market, making the renting prices competitive. Also, the global GPU as a Service market is expected to grow at an impressive CAGR of 40.8% by 2033 as per a research report by Future Market Insights Inc. So, you can benefit from this growing trend.

Best Practices for Using Rented GPU for AI Development

Optimum resource utilization is the key to deriving maximum benefits by renting GPUs. So, you need thorough project planning and monitoring to make the most of your rented GPUs and stay on track in your AI development. Below are some of the best practices we utilize when working with rented GPUs for our AI projects:

Define Clear Requirements: You need clarity on the AI development tasks you want to complete using the GPUs. So, define computational requirements based on these tasks as different GPU models will be suitable for varied tasks. Another important criterion is checking the compatibility of the GPU you are renting with your preferred AI frameworks like PyTorch, TensorFlow, etc., for seamless integration. This helps you in selecting the right GPU models with the specifications required for your needs.

Optimize GPU Usage Time: Develop a detailed timeline for each stage or step of your plan to optimally utilize rented GPUs. Look for offers from different GPU providers so you can rent them only for the time you need. If you anticipate idle periods, opt for the pay-as-you-go model. This will keep a check on costs and help you stick to the project budget. Also, if you are experimenting or working on a short project, this pricing model is the right choice.

Leverage the Latest Tech: Rental services keep updating their offerings with the latest GPUs. You can experiment with different models and check which best suits your project requirements. And since you are renting and not buying a GPU, you can switch to the latest and more capable model to complete your tasks.

Track Performance Continuously: Utilize monitoring tools or other methods to continuously monitor the performance of your rented GPU during the project. It helps you identify bottlenecks early and assess the model’s usage pattern and computing efficiency. So, you can make timely adjustments to ensure your project's success.

Implement Security Measures: Your project data could be sensitive and require extra protection especially when using third-party cloud services. Check what security measures your GPU rental provider uses and add your own for an additional layer of data protection and regulatory compliance.

Choose the Right Rental Service: GPU rental providers often offer maintenance and support which is critical to keep your operational cost and burden low. They each offer different pricing and rental duration models. You may have scalability requirements, wherein you may need to rent additional GPUs for a longer period. Analyze every aspect of the offerings from various providers carefully and align them with your project needs to make an informed decision.

Leverage Our Expertise in GPU Utilization for AI Development

We’ve discussed several benefits of renting GPUs and even shared the best practices for the same based on our experience. However, staying in the know is the key to effective utilization of evolving tech like GPU.

We understand that you need to focus on your core business rather than spend time on the intricacies of which GPU model to use for your AI project or how to rent it. That’s why, at TenUp, we take complete ownership of our AI projects and ensure our clients get value for their money.

Our AI consultants thoroughly assess your needs and recommend solutions to ensure scalability and cost-effectiveness in AI development. You can explore our comprehensive AI Services and Solutions as well as our Case Studies to get a better understanding of our capabilities. We have built several AI-based solutions across industries that have delivered real results and satisfied our clients.