Options to raise funds for companies engaged in ML training on GPU.

Machine learning is a promising industry that is actively developing. A particularly popular method is training models on GPU, which has shown high efficiency compared to central processing units (CPUs). However, in order for the company to start implementing its ideas, funding is needed. Machine learning is a costly industry that requires large investments. We will tell you about the main ways to raise funds and help you choose the best one.

Why is machine learning expensive?

ML training on GPU is really promising as it is the technology of the future. Experts expect that in the near future, this technology will have an impact on various areas of our lives, from unmanned vehicles to robotics for space exploration. However, before starting to implement an ML project, large startup capital is required. A company using GPUs for training models needs:

• Hardware. These are GPUs, boards, and much more, without which the machine learning process is impossible. It is important to understand that you need a lot of components, and their price is high.
• Servers. Machine learning requires the processing and storage of huge amounts of data. Therefore, it is necessary to include the cost of long-term server rental in the budget.
• Data centers. Machine learning requires cloud computing and needs the performance to match. Therefore, it is necessary to rent the capacities of the data center.
• Software. Any equipment can only work if the appropriate software is available. Machine learning requires specialized, adapted software.

Thus, before starting the project implementation, it is necessary to work out a detailed business plan. At the same time, it is important to foresee all expenses in the long term and provide a reserve amount of funds, which will help solve the problem of unplanned expenses for companies using GPUs for training models.

How can funding be raised?

Companies engaged in ML training on GPU have the opportunity to raise funding in four main ways. Let's consider them in more detail.

1. Private funding rounds

The first way to raise funds is through private funding rounds. This option is focused on raising funds through specialized funds or institutional investors. The idea is to get funds from one or two investors who will fully meet the startup's needs in the long run.

The main difficulty of this method lies in the search for interested parties. Institutional investors are reluctant to invest in such projects because the degree of risk is very high.

1. IPO

An IPO is raising funds by issuing shares on a stock exchange. Investors buy shares of the company and thus provide its financing. In turn, the company undertakes to fulfill its obligations, pay dividends in a timely manner, etc.

The key difficulty with an IPO is that it is very expensive to list a company. The preparation cost will be very high, and it is not certain that the company will be able to raise enough funds. There is also a limitation in terms of the form of ownership of the organization. Only open joint-stock companies can take part in an IPO. If the company is closed, it will have to change the form of ownership. On average, preparing for an IPO takes 12 months. Thus, an IPO is an option for already established companies that are successful in the market and want to expand or enhance their business. This is not the right method for an ML startup that is starting from scratch or for a small company.

1. Bank loans

Another possible method of raising funds for companies that work with machine learning is obtaining a loan from a bank. However, modern financial institutions remain quite conservative organizations. To get a loan, companies must convince the bank that their idea is viable and they can repay the loan. However, many banks do not have a risk assessment methodology for ML businesses. Even if they give out a loan, they will offer a very high interest rate, trying to play it safe.

1. STO

Security Token Offering (STO) is a relatively new way to raise funds. It is gaining popularity among companies working in various fields, including machine learning. As part of the STO, the company offers investors security tokens. Unlike utility tokens, they are officially regulated by government agencies in different countries and provide guarantees for investors.

Security tokens allow clients to get the right to a share of profits, to make decisions within the platform, etc. Compliance with these guarantees is ensured by the technology of smart contracts, which prescribe all the necessary conditions.

Why is STO better for GPU machine-learning companies?

Each of the above options has certain advantages. However, it is the STO that has the most benefits for innovative companies. Thanks to this method, it is possible to create a clear structure for investors when they know how they invest, how much profit they will receive, and in what time frame. STO has a number of important benefits, including:

• Opportunity to raise funds from private investors. Private investors can provide a startup with the necessary level of funding through relatively small but massive investments from many investors.
• The risk appetite of private investors. Machine learning is a high-risk industry. Private investors, unlike institutional ones, are more willing to invest in such assets. They often add risk assets with potentially large profits to their portfolio.
• Guarantees for investors. Smart contract technology provides reliable protection for investors. Conditions for the execution of transactions are set before the STO.
• Legal basis. Security tokens are regulated in the United States and many other countries around the world like securities. This is an important factor in favor of reliability.

In addition, the choice in favor of STO does not require a change in the form of ownership of the company. It does not need to change charters and internal documents, become an open joint-stock company, and so on.

Summary

ML training on GPU is getting a lot of attention. There are many companies with great ideas on using GPUs for training models, but they face funding problems. The costs of training models on GPU are high, and the money is hard to find. There are four main ways to attract investments, and the most promising of them is STO. STO allows both investors and the company to get exactly what they need. Business gets funds, and investors receive guarantees of profit and legal protection of interests. Thus, it is STO that can be considered the most suitable option for companies that intend to work in the field of machine learning on GPU. If you want to learn more about tokenization and STO launch, feel free to sign up for an initial consultation with Stobox experts.