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By using a standard, albeit powerful GPU unit, you already increase the capacity of your computer to perform complex calculations and explore ML learning. But as you progress with your Machnie Learning skills, sooner or later you’ll need to test them on the more advanced models. More hashing power will become necessary, so let’s explore the options you have to get access to it at a fraction of the price.

Why do so many people start learning ML?

Machine Learning is among the most desirable skills across industries right now. What is more, there’s a very slim chance it will change in a foreseeable future. Machine Learning scientists and engineers are needed practically everywhere. They can also find work as Business Analysts, Data Scientists, NLP Scientists, and in myriads of other high-paying job positions.

As of 2024, ML scientists at Amazon can make an average of $215,000, which is an incredible amount for a skill that didn’t even exist some 20 years ago! And if you’re currently learning it at a BootCamp or via the self-taught route, you probably know that this work is also a lot of fun. But without a good GPU, your dream career might not arrive, so let’s talk about the ways someone can lease GPU for your projects.

One of the main reasons why so many people start learning Machine Learning is its increasing prominence in the technology industry. With the advent of big data and advancements in computing power, ML has become a crucial tool for businesses to gain insights, automate processes, and make better decisions.

Moreover, the demand for ML professionals is only expected to grow in the coming years, as more industries adopt the technology to stay competitive. Aside from the promising job prospects, many people also find the field of ML fascinating and intellectually stimulating.

It allows them to work on cutting-edge technologies and solve complex problems using mathematical and statistical models. Additionally, the community of ML practitioners is highly collaborative and supportive, making it an exciting field to be a part of. However, one of the major challenges in learning ML is the requirement for high-performance computing resources, particularly GPUs.

These resources are essential for running complex algorithms and training models on large datasets. As such, many aspiring ML professionals may find it challenging to access the necessary computing resources for their projects. Fortunately, there are various ways to lease GPUs and other cloud-based computing resources to overcome this challenge.

A dedicated GPU server rental – how does it differ from the shared one?

As you already know, a standard server provides computing resources such as processing power, memory, and storage, to support applications and services. GPU servers are in general a bit different in that they use several GPU units from the best manufacturers and just a little CPU to perform the operations.

They can be easily harnessed for your ML experiments by utilizing reliable GPU rental company services. You will pay for the GPU hashing resources by the hours, but the task of maintaining the hardware – not to mention buying it! – won’t be your problem. The GPU server rental company such as Hashmarket will take care of that and make sure the resources are available to you at any given moment.

These solutions, however, often employ shared servers. A dedicated GPU server, on the other hand, is a server that is specifically prepared to serve one user or one company. It’s set up with powerful graphics processing units (GPUs) for high-performance computing tasks often required in ML projects. If you choose to rent such a server, you can rest assured that it will be optimized for tasks that require large-scale parallel processing and the ability to handle massive amounts of data.

Is it a good idea to rent a GPU dedicated server for practice?

If you’re new to Machine Learning, you’re probably starting with very simple problems that don’t require that much computing power. So at the very beginning of your journey, you might get away with simpler and cheaper solutions like shared servers that don’t require as much GPU capacity. But remember all the time why you’ve even started this arduous and rewarding career path.

You’ve got your eyes set on working for the FAANG company somewhere down the road? Then you already know you need to be in the 1% of the best with your skills, creativity, and dedication.

And this will require much more experience in handling complex ML models or NLP networks that you can gain with limited computing power. A powerful GPU rental dedicated specifically for you will need some financial sacrifices, but it will also help you build high-level projects you will be truly proud of in the future. These projects, in turn, will make a fine addition to your ML portfolio and significantly improve your chances of getting a coveted job in the field.

Summary

Using a dedicated GPU server for machine learning offers several advantages, especially when working with large datasets and complex models. Some of the key benefits include:

1. Faster computation: GPUs are specifically designed for parallel processing, which is a common requirement in machine learning tasks. They can handle thousands of threads simultaneously, speeding up the computation of mathematical operations, and ultimately reducing the training time of your models.
2. Scalability: GPU servers usually have multiple high-performance GPUs, allowing you to scale your workloads horizontally. This means you can train multiple models in parallel or distribute a single model’s training across multiple GPUs, which can significantly decrease the training time.
3. High memory bandwidth: GPU servers have a high memory bandwidth, which allows for faster data transfers between the GPU and its memory. This is particularly beneficial for machine learning tasks that require large amounts of data to be processed quickly.
4. Specialized libraries and frameworks: Many machine learning libraries and frameworks, such as TensorFlow, PyTorch, and CUDA, are optimized for GPU computing. This allows you to leverage the full potential of the hardware and simplify the development process.
5. Cost-effectiveness: Although the initial investment in a dedicated GPU server might be higher compared to a CPU-based server, the long-term cost savings can be significant. Faster training times and increased productivity can offset the initial cost, making it a more cost-effective solution in the long run.
6. Energy efficiency: GPUs are more energy-efficient than CPUs for machine learning tasks, as they can perform more operations per watt.