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Tokenization options for businesses using GPUs for machine learning
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Tokenization options for businesses using GPUs for machine learning

GPUs are often used for machine learning. Still, purchasing the necessary number of video cards is not enough, and companies need investments to implement ML projects.

Machine learning is an industry that is actively developing. This technology has made fantastic progress over the past 10 years, and its future is impressive, so this area is gaining a lot of attention. However, getting started in the machine learning industry or expanding the capabilities of an existing business requires substantial financial resources. This is especially true for GPUs for machine learning. Many startups are faced with the problem of raising capital because it can be very difficult to do it in traditional ways.

However, there are ways to get the necessary budget. One of them is asset tokenization. Tokenization is becoming an increasingly popular way to raise funding for companies, as it opens up more opportunities than traditional tools. In this article, we will explain how tokenization can help in the field of machine learning on GPUs.

Why companies use GPUs for machine learning

Power is the key advantage of a graphics processing unit (GPU) over a central processing unit (CPU). The GPU is 200 times higher than the CPU. Even though the CPU core itself is more powerful, this power is simply not used and cannot be used by machine learning applications. The range of tasks that this processor performs is much wider. Therefore, the possibilities for its use are limited.

In turn, the GPU does not solve as many tasks as the CPU, so its power is available. GPUs can handle a massive amount of data. The performance of such chips can exceed that of the CPU by 10 times. Therefore, GPUs for machine learning are considered the most effective solution today.

What else is needed for machine learning?

The GPU is an essential cost item for a startup looking to get into machine learning. Modern manufacturers produce special graphics processors explicitly designed for this purpose. Their peculiarity lies in the presence of additional tensor cores, which allows for increased power. For example, NVIDIA has such developments. Google and Intel promise to introduce similar technologies soon.

However, a company needs not only to purchase the required number of the best GPUs for machine learning that meet its requirements. The company needs to create a computing stack. It includes the following components:

  • Servers. Special servers are needed that can provide adequate performance. These are devices with unique optimization that can quickly receive and process data.
  • Data centers. They are tasked with performing complex calculations, so appropriate characteristics are required. Today, it is possible to rent computing power: for example, such services are provided by Google, Intel, or Cray.
  • Software. Optimized frameworks and models are required to ensure the GPU’s work. Cloud providers are gradually optimizing their software for the needs of developers, but so far, the offer is not very versatile, and the cost of such services can be quite high.

The number of products and service providers for machine learning is gradually increasing. However, any ML company will face enormous expenses at the beginning of the journey. So, the issue of attracting funding for such projects is particularly acute.

GPUs for machine learning

Why do companies face the challenge of raising funding?

Machine learning is a promising but high-risk industry. This imposes significant restrictions on the possibility of attracting funding. Traditional sources, such as banks or institutional investors, are not ready to allocate the necessary amounts as the risks are too high. Even if you find a bank willing to give a loan, the conditions will be unfavorable for your company. The bank will offer an increased interest rate to minimize the risks, making things tough for a company.

You can go in alternative ways – raise money through venture funds or receive a grant. Still, these ways are quite complicated. First of all, you need to find the right program. You must present your project to the fund’s representatives or the grant committee and convince them that this is a critical development with prospects. Then it takes time for the application to be considered. However, there is no guaranteed result: a venture fund or a grant committee may refuse, and your business will lose time.

How does tokenization solve the problem of funding for ML companies?

One way to raise funding for machine learning companies is to tokenize assets. Tokenization is the process of transferring certain assets to the blockchain. Almost any asset can be tokenized. In particular, it can be company shares, thanks to which tokenization can solve many problems of ML companies.

In fact, tokenization is the creation of a digital analog of shares. Thanks to this, a company can attract funding, and investors get the right to obtain a part of the company’s profits. They may also have other rights, such as voting on the company’s business processes.

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Tokenization involves the issuance of unique security tokens. In many countries, this type of token is considered a security and is regulated accordingly. Compliance with the transaction terms between the investor and the issuing company is guaranteed by smart contracts technology.

Benefits of asset tokenization for ML companies

Asset tokenization offers a range of benefits for both companies and investors. The main advantages of such a solution are as follows:

  • Opportunity to attract financing from private investors. The company does not need to look for a prominent institutional investor. It can raise funds through investments from small private investors.
  • Interest from a risk-averse audience. Unlike banks or venture capital funds, private investors are willing to take risks for the sake of big profits. Therefore, they eagerly add high-risk assets to the investment portfolio.
  • Raising funds for companies in any form of ownership. Only open joint-stock companies can hold an IPO. There are no such requirements if a company conducts a security token offering (STO). However, you can also create an SPV to facilitate the legal side of the offering.
  • The ability to include any conditions in a smart contract. For example, an issuer can determine how long an investor must store a token to receive their share of the profit. 
  • Investor protection. Investors are willing to invest in security tokens because regulators protect their rights. Governmental bodies in many countries define such tokens as securities and apply the corresponding requirements.

Thus, asset tokenization is a promising path for companies using video cards for machine learning. It can attract financing even for the most complex and risky projects.

Summary

Machine learning opens up new horizons for humanity. Such technologies make it possible to create unique products, from unmanned taxis to complex space exploration vehicles. Special GPUs for machine learning are often used, which require particular infrastructure. There are a lot of ideas related to machine learning. However, often at the stage of their implementation, companies face financing problems.

Asset tokenization solves the problem of attracting investments. Such a step allows you to receive funds from private investors under a guarantee that your company will fulfill your obligations. Tokenization is a promising investment attraction method that is actively developing, and it has already attracted the attention of startups working in the field of machine learning. If you want to learn more about tokenization, contact Stobox experts for an initial consultation.

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