Maximizing ROI: The Business Case for Renting GPUs

The race for AI has become more intense now than ever as big tech companies like Google and Microsoft are integrating AI into their search engines. On the other hand, billion-dollar companies like OpenAI are continuously improving their products and allowing other companies and individuals to build on them. 

The good thing about these big companies is that they have multiple investors and the financial muscles to invest in the latest technologies and GPUs to power their AI models. However, if you are a startup you may not have the same resources to invest in hardware that can handle AI training. Luckily, you still have the option of renting GPUs and building AI models.

This post will highlight the financial and operational advantages of renting GPUs versus purchasing them outright, specifically for businesses involved in AI development, machine learning, and high-performance computing

Cost Efficiency – Reducing Capital Expenditure

Many of the big AI applications are powered by high-performance GPUs like Nvidia’s A100 or H100. However, the cost of acquiring these chips is high as Nvidia’s A100 costs roughly $10,000, while Meta spent US$10 billion on 350,000 NVIDIA H100 chips. We can also see companies like Microsoft, Amazon and Google building their own proprietary chips. For many companies, especially startups or those scaling rapidly, this upfront cost can be a barrier to growth. 

Aethir offers cost-effective GPU rental solutions, reducing the financial burden on businesses. Aethir’s distributed network reduces the costs associated with legacy providers by as much as 80%. By renting GPUs from Aethir, companies can pay for only the compute resources they need, allowing them to reinvest savings into other critical areas of business, such as R&D or scaling their AI models.

Maintenance-Free Operations – Let Us Handle the Hardware

Maintaining high-performance GPUs involves hardware upgrades, cooling systems, power management, and potential downtimes. Companies might be required to adjust their power supplies as chips like the NVIDIA H100 Tensor Core GPU consume 350 to 700 watts. The same GPU can also produce 700W heat output. Technical expertise is required to maintain GPU hardware, which can strain internal IT resources. All these things add to operational costs.

Aethir’s distributed GPU infrastructure is fully managed, meaning businesses don’t have to worry about maintenance or downtime. This allows AI enterprises to focus on what they do best—building and deploying AI models—while Aethir handles all the backend complexities. Users can now train state-of-the-art AI models by renting GPU time from the decentralized pool.

Scalable Solutions – Flexibility for Dynamic Workloads

AI workloads are often unpredictable. If you are training AI models, you may find there are times when you need a lot of GPUs while at other times they remain idle. There are instances where you spend a lot of time training the models, while in others, you are fine-tuning or testing these models. While GPU demand may spike during certain projects or times of the year, it may not be consistent.

Purchasing GPUs to meet peak demand can lead to idle resources during off-peak periods, reducing overall ROI. With Aethir, companies can scale their GPU resources up or down based on project needs. This flexibility ensures businesses only pay for the computing power they need, optimizing costs. Whether handling a large AI model training workload or scaling back during off-peak periods, Aethir’s rental system adapts to changing business demands.

Access to Cutting-Edge Technology – Stay Ahead of the Curve

Technology is ever-evolving, and so is the GPU market. The NVIDIA A100 was announced on May 14, 2020. However, there are more than five variations of the same series. Companies that purchase GPUs like NVIDIA A100 or H100 may find their hardware outdated within a few years.

We have already seen that GPUs are expensive. You may also not know what it takes to select the right GPU for your AI workload. However, keeping up with cutting-edge technology is critical for AI development. DePINs like Aethir create a network where users get access to the latest GPU models and make the power of AI accessible to everyone. Users don’t have to worry about acquiring new GPU models as Aethir creates a platform allowing them to access cutting-edge technology cheaply. 

Sustainability – Lower Costs, Lower Carbon Footprint

Greenhouse gas emissions have become a concern in the modern GPU world. GPU clusters consume a significant amount of energy, which not only impacts operational costs but also contributes to a company’s carbon footprint. An NVIDIA A100 GPU, for instance, can produce up to 1,800 kg CO2 carbon footprint yearly. 

However, the world is falling in love with companies focused on Environmental, Social, and Governance (ESG) goals. Aethir’s globally distributed GPU network is designed with energy efficiency in mind. Companies renting GPUs from Aethir can reduce their energy consumption by utilizing Aethir’s eco-friendly data centers, which are optimized for energy efficiency. This reduces both operational costs and environmental impact, helping businesses align with their sustainability goals.

Maximize ROI with Aethir's GPU Rentals

It is quite evident that renting GPUs can significantly increase ROI by lowering upfront costs, reducing maintenance burdens, providing flexible scalability, and ensuring access to the latest GPU technology. This approach also lowers the entry barriers into the artificial intelligence space as startups and AI enthusiasts get affordable and on-demand GPU services. 

Interested in learning more about GPU rental services? Contact Aethir for a consultation to determine how renting GPUs can improve your business’s ROI and align with your operational needs.