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How to make your machine learning project more profitable with tokenization
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How to make your machine learning project more profitable with tokenization

Find out how machine learning companies can grow and receive investments faster with tokenization.

Graphics processing units (GPUs) are used in various operations to process large amounts of data, including 3D rendering and mining cryptocurrencies. Another application area for GPUs is training neural networks using deep learning technologies. Using GPUs for training models has its peculiarities, advantages, and disadvantages. Below, we’ll explore how GPUs are being used for machine learning and how tokenization can help companies that process large amounts of data.

How do graphics cards help in machine learning?

Machine learning of neural networks has long gone beyond scientific laboratories. Today, machine learning is used in many industries. The spread of such technologies has become possible due to the availability of large amounts of data and more efficient processing of them. GPU computing plays an important role in this.

Machine learning on GPU is used for image classification, word processing, and speech recognition.

GPUs use large training sequences in a short period to train neural networks. Also, with the help of video cards, machine learning training models are reproduced to perform classification tasks.

Key benefits of using graphics cards for machine learning

One of the main advantages of GPUs is performance. Due to the specific architecture of the core, video cards quickly process a large number of similar tasks. This is how they differ from the CPU.

  • It may take months to train a neural network using a CPU. If you run this process on video cards, they will do the job in a few days. At the same time, they consume less electricity, as they need less infrastructure.
  • Parallelism. Neural networks are exactly parallel algorithms, so GPUs are great for machine learning.
  • Video cards are optimized for matrix operations and make them faster – neural networks need them to get results.

As for the drawbacks, the use of video cards for machine learning also has them. Deep learning requires a lot of computing resources, so it is vital to choose the best GPU for machine learning. You also need to understand that if you are not just an amateur but are engaged in machine learning at a commercial level, you will need to assemble large rigs from video cards and set up infrastructure. You may face the lack of powerful graphics cards and infrastructure that you need on the market. You also need to be prepared for high electricity bills and the fact that this industry is capital intensive. Whether you are just planning to start a video card farm or want to expand your business, you must have sufficient capital or raise funds from investors. The latter can be problematic, but tokenization can help solve this issue.

best GPU for machine learning

How tokenization helps machine learning businesses

As we wrote above, building a machine learning business is quite expensive. Companies starting from scratch or looking to expand are often in search of funding. Classical businesses have essentially two ways to raise finance: private investors and bank loans. Both of these paths cause difficulties for companies associated with creating rigs for processing arrays of data, whether it is cryptocurrency mining, 3D rendering, or machine learning. In particular, banks often do not have a risk assessment methodology for such cutting-edge technologies.

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If you choose to raise funds from private investors, you should understand that finding interested persons in your project will take a lot of time. You will have to face long negotiations, the need to prove that your project is viable and will be able to generate income, and many refusals. Tokenization is a profitable solution for many businesses operating in the field of new technologies.

Tokenization is the transfer of ownership in the blockchain. The result of tokenization is the issuance of security tokens, which are considered security in many jurisdictions. This protects investors and gives token issuing companies a legal basis to launch an STO (security token offering).

By issuing security tokens during the STO, the company can sell them to interested investors worldwide. It is also possible to create whitelists to sell tokens only to selected investors. The token will correspond to the investor’s right to receive a certain percentage of the company’s profits. In general, an STO is similar to an IPO. The difference is that an STO takes an average of 3-4 months from preparation to the issuance of tokens and is much cheaper than an IPO, which takes about one year to prepare. The STO provides the following benefits:

  • Increasing liquidity. A company can issue tens of thousands of tokens, each of which will correspond to a certain share of the company and cost, for example, from $500. This opens up more opportunities for attracting small investors who do not have a large budget.
  • Opening of the secondary market. If an investor wants to exit, they can do so at any time by selling the token to other interested parties.
  • Compliance with legal requirements in key jurisdictions. Previously, many projects launched ICOs to raise funds quickly. However, there were often problems because the financial regulator considered ICOs to be offerings of securities, which entailed fines for companies and sudden closure of offerings. Security tokens are initially regulated as securities, so such problems will not arise. However, companies require legal advice to select the appropriate jurisdiction to launch an STO.

Related: STO market history: from origin to mass adoption

Summary

Companies engaged in GPU machine learning often face the challenge of raising funds to expand and purchase more powerful hardware. Traditional financial institutions simply do not know how to assess the risks associated with the latest technology. One of the ways to raise funds relatively quickly and easily is an STO, a security token offering. By tokenizing your company’s assets, you offer investors tokens that are eligible for a sure profit. If you want to learn more about the nuances of tokenization and how it can help your business grow faster, you can contact our experts. We offer an initial free consultation for our potential clients.

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