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VPS vs Physical Server: What is Better

VPS vs Physical Server: What is Better
Hostman Team
Technical writer
Infrastructure

Simply put, a server is a remote computer that is used by developers and webmasters as a software platform to store their apps and sites on.

When you want to deploy any online product to the World Wide Web you have to host it on a server that works 24/7 and make it available to any person from any part of the globe.

There are two types of servers. Physical ones that are actual computers with real hardware in them like one that you use but much more powerful. And virtual ones that are based on similar hardware of one vigorous PC running permanently but are in fact copies of real operating systems used as a substitute for physical servers with the same capabilities.

Now let’s get deeper into how different types of servers work. How are they structured in terms of technologies, which ones are most effective and which you should you choose for your project.

What are physical servers?

Sometimes it is called a "dedicated server". As we mentioned above, a physical server is a real computer with tangible hardware parts. It has a processor, a certain amount of RAM, a disk to store data on (SSD or HDD), a lot of connectivity ports, and stuff like that.

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It works like any PC, so it is possible to launch applications on it. And you can do so remotely. Some providers have plans with dedicated servers for rent. In summary, the process of working with this type of server is as follows:

  1. You pay in advance to access the server.

  2. Hosting gives you a pre-made machine with a certain amount of memory and other hardware components.

  3. You connect your device with a rented remote PC via a technology called SSH (or it's alternative) and control it as if it were your real computer.

Pros and cons of physical servers

The main advantage of using a dedicated server lies in the ability of the renter to control every aspect of the "machine". You have full access to anything software-wise, so you can use tools that are not available on IaaS-platforms or on virtual servers. DS comes with no restrictions at all. Also, such an approach gives you more security because nobody except you can access data inside a rented computer. Even a provider who can destroy your machine physically cannot override your privileges to control it and erase or alter data stored on the server.

There are also disadvantages in opting for such a solution. First off, it is expensive. Dedicated servers are costly to maintain so providers will charge you more, and you can’t do anything about this. Secondly, it is hard to scale a physical server when the audience for your application or the size of the database exceeds the already ambitious expectations you had when you rented it. It would be hard to move data from the old machine to the new one or to upgrade the current PC without shutting it down for maintenance.

Data backup on physical servers

One of the problems inherent in hosting products on a physical machine is the lack of basic tools to protect the data and duplicate it somewhere else in case of any malfunction.

To resolve this you might want to use software called Veeam. To back up a physical server developers have to add computers to a specific location named "Protection Group". This is possible via Veeam Backup and Replication tool. You have to add all the machines whose data you are going to add to the backup.

Then in the same application, you’ll be able to create a "Backup Job". It is a process that automatically gathers all the information from PCs included in the Protection Group.

What are virtual servers?

Virtual servers are simulacrums of physical ones. Sets of hardware and software technologies emulating real computers with the same capabilities you’d normally expect from them.

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They are also used to host websites and applications but in addition to traditional applications cloud technologies and different ways of virtualization introduced products that help to create fully functional digital workspaces, massive data storages, etc.

There are three main types of virtualizations:

  • OS-Level Virtualization — when the server containerizes certain applications or specific areas of OS, multiplies it, and makes it available for renters to install some software to work with.

  • Full Virtualization — it is a more complex virtual that is usually launched on bare metal (real computers hardware) using RAM, processor, and disk that exist and are not emulated.

  • Para-virtualization — once the hardware is used to install it many virtual OSes are controlled via special utilities called hypervisors.

Benefits of server virtualization

Different types of virtualization come with different advantages.

The most obvious one is saving. All three help providers to spend less money on actual hardware. They can create as many virtual servers on one computer with one set of RAM, ROM, etc. Artificial platforms like this are cheaper as a product for developers and webmasters. But at the same time, emulated servers have the same capabilities as real computers.

Moreover, para-virtualized solutions have enough security to work with sensitive data. And OS-level containers make it simple to scale the system as a whole.

Different ways of virtualization yielded different products based on it that are now used by hosting providers.

Virtual Private Servers

VPS is a product of para-virtualization. It is a server that "tries hard" to become a full-fledged computer and makes it possible to use any operating system and any tools that you wish to access on your remote server.

VPS is actively used by developers and webmasters around the world to:

  • create from low- to middle-level informational sites, online stores, commercial websites.

  • develop and test applications together with the team.

  • to host personal gaming servers.

  • to store databases.

It is quite multi-purposeful and most of the time the VPS’s capabilities would strongly depend on the plan that you chose renting the server (and your aims of course).

How does VPS work

Virtual Private Servers differ from each other by the technology that is used to create and control virtual machines. Usually, VPS is divided into two camps: based on OpenVZ tech and KVM tech. We’ve already discussed containerization, so OpenVZ is similar in terms of implementation. It makes it possible for a provider to host many virtual machines on one PC making each of them dependable on the same CPU, the same RAM, and the same disk. KVM is different because it helps to create many isolated artificial servers on one physical one. It is a much more secure and reliable technology.

Advantages and disadvantages of VPS

Pros of VPS are:

  • Relatively low price. It is not as cheap as basic virtual hosting but it costs less than a physical machine. Considering the fact that it behaves more like a real computer it seems to be a good bargain.

  • Less responsibility. You don’t really have to worry about the technical aspects. Just launch a server with a desired OS and applications and you are ready to go.

Cons of VPS are:

  • Dependency on shared hardware. Some types of VPS give you only a fraction of the hardware installed on a physical machine. In this case you’ll share it with other developers and webmasters. It sometimes means your application or website might performan poorly because of other tenants using the same server. Even if you choose proper virtualization technology, hypervisors will limit potential capabilities.

  • Also, you have no impact on hardware itself. You’re isolated inside the workplace guaranteed to you by a hypervisor.

Virtual Dedicated Servers

If you see the abbreviation VDS don’t bother looking for an explanation. It is basically the same technology represented by VPS. The only distinction you might stumble upon is a type of virtualization used for both. Webmasters sometimes like to emphasize it. Just as VDS only means KVM-like servers and VPS OpenVZ-like. More on this in our previous article.

Shared hostings

The most basic type of virtual servers. The idea of it is similar to containerization but on a more subtle level. Basically, when you rent shared hosting you get a space on the hard drive of a PC controlled by administrators of the host you pay.

It is not as bad as it might seem at first. Yes, your server in this case is just a folder. But nobody restricts you from putting files in it. It is therefore a mediocre but practical solution for simple websites (there are many devs out there who build on top of shared hostings rather massive WordPress-based projects).

If you need something more complex that requires installing different packages or using specific structures you should rent VPS and go a step further.

Cloud-based solutions

The highest degree of virtual servers. Clouds are ephemeral and outstandingly flexible. Virtually you can convert them into anything. Database, application server, digital office, private file storage, a stack of private clouds together forming hosts to deploy different tools on, etc.

On top of clouds, different companies build fully-fledged services that are almost ready to use before you click "buy". And they are separated into three groups:

  • IAAS — Infrastructure as a Service. A type of cloud where everything hardware-wise is on hosting and anything software-wise is on the tenant. A solid solution for a strong team of developers wishing to focus on development but not on server management.

  • PAAS — Platform as a Service. A more abstract form of service that cuts out part of the software management and puts developers eye to eye with the OS-level fragments of the infrastructure.

  • SAAS — Software as a Service. A modern solution for teams that have no need for OS-level control but rather certain software solutions. For example, Hostman offers pre-made virtual cloud clusters with analytic tools, gaming servers, databases, and other stuff that developers and entrepreneurs need for their work but have no competence to implement manually (or just don’t want to).

Data backup

Using modern virtual servers you don’t really have to worry about data safety. The host will take care of it (normally once you’ve pay for it). And it works not only for modern solutions like cloud-based ones but for classical VPS servers too.

To create backups you will need to access a control panel (a special tool provided by the host that lets interact with your server). In 99% of cases this will be a button or a tab saying "Create a backup" or something like this. Activating it will quickly and effortlessly create a copy of every bit of information on your server that you need. Moreover, you will probably to able to plan this procedure so it happens automatically every few days.

Conclusion

Here it is. It is of course up to you to choose what kind of server to use as a host but think twice before making a decision. Virtual platforms are highly anticipated because they are really easy to operate and powerful. Want to try one before paying for anything? Get to Hostman Marketplace and choose a virtual platform with a preinstalled software of your choice or deploy your own via GitHub. Everything is free for 7 days and after that prices start at just $5.5 per month.

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“New electricity”—that’s what many people call artificial intelligence today. Some see AI as another tech bubble, while others believe our lives will become unrecognizable within five to seven years. We’re already seeing AI become part of everyday life, often without realizing it. For example, every modern search engine not only shows relevant links but also tries to directly answer your question. The growing popularity of AI is closely tied to the rise of chat interfaces, which gradually came to be known as AI assistants. In this article, we’ll take a detailed look at the best AI assistants, explore their key features, and see how these technologies are changing our lives. The Evolution of AI: From Narrow Assistants to Autonomous Agents At first glance, all AI assistants might seem similar, but they can actually be divided into several categories based on their level of autonomy. An AI assistant is primarily a reactive system that performs direct user commands. 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GPUs for AI and ML: Choosing the Right Graphics Card for Your Tasks

Machine learning and artificial intelligence in 2025 continue to transform business processes, from logistics automation to personalization of customer services. However, regular processors (CPUs) are no longer sufficient for effective work with neural networks. Graphics cards for AI (GPUs) have become a key tool for accelerating model training, whether it's computer vision, natural language processing, or generative AI. Why GPUs Are Essential for ML and AI Graphics cards for AI are not just computing devices, but a strategic asset for business. They allow reducing the development time of AI solutions, minimizing costs, and bringing products to market faster. In 2025, neural networks are applied everywhere: from demand forecasting in retail to medical diagnostics. GPUs provide parallel computing necessary for processing huge volumes of data. This is especially important for companies where time and accuracy of forecasts directly affect profit. 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Characteristic Description Significance for ML VRAM Volume Memory for storing models and data Large models require 24-80 GB CUDA Cores / Tensor Cores Blocks for parallel computing Accelerate training, especially FP16 Framework Support Compatibility with PyTorch, TensorFlow, JAX Simplifies development Power Consumption Consumed power (W) Affects expenses and cooling Price/Performance Balance of cost and speed Optimizes budget Video Memory Volume (VRAM) VRAM determines how much data and model parameters can be stored on the GPU. For simple tasks such as image classification, 8-12 GB is sufficient. However, for large models, including LLMs or generative networks, 24-141 GB is required (like the Tesla H200). Lack of VRAM leads to out-of-memory errors, which can stop training. Case: A fintech startup uses Tesla A6000 with 48 GB VRAM for transaction analysis, accelerating processing by 40%. Recommendation: Beginners need 12-16 GB, but for corporate tasks choose 40+ GB. Number of CUDA Cores and FP16/FP32 Performance CUDA cores (for NVIDIA) or Stream Processors (for AMD) provide parallel computing. More cores mean higher speed. For example, Tesla H200 with approximately 14,592 cores outperforms RTX 3060 with approximately 3,584 cores. Tensor Cores accelerate low-precision operations (FP16/FP32), which is critical for modern models. Case: An automotive company trains autonomous driving models on Tesla H100, reducing test time by 50%. For business, this means development savings. Library and Framework Support (TensorFlow, PyTorch) A graphics card for AI must support popular frameworks: TensorFlow, PyTorch, JAX. NVIDIA leads thanks to CUDA, but AMD with ROCm is gradually catching up. Without compatibility, developers spend time on optimization, which slows down projects. Case: A marketing team uses PyTorch on Tesla A100 for A/B testing advertising campaigns, quickly adapting models to customer data. Power Consumption and Cooling Modern GPUs consume 200-700W, requiring powerful power supplies and cooling systems. In 2025, this is relevant for servers and data centers. Overheating can lead to failures, which is unacceptable for business. Case: A logistics company uses water cooling for a GPU cluster, ensuring stable operation of forecasting models. Price and Price-Performance Ratio The balance of price and performance is critical for return on investment (ROI) and long-term efficiency of business projects. For example, Tesla A6000, offering 48 GB VRAM and high performance for approximately $5,000, pays for itself within a year in projects with large models, such as financial data processing or training complex neural networks. However, choosing the optimal graphics card for neural networks depends not only on the initial cost, but also on operating expenses, including power consumption and the need for additional equipment, such as powerful power supplies and cooling systems. For small businesses or beginning developers, a graphics card for machine learning, such as RTX 3060 for $350-500, can be a reasonable start. It provides basic performance for educational tasks, but its limited 12 GB VRAM and approximately 3,584 CUDA cores won't handle large projects without significant time costs. On the other hand, for companies working with generative models or big data analysis, investing in Tesla H100 for $20,000 and more (depending on configuration) is justified by high training speed and scalability, which reduces overall costs in the long term. It's important to consider not only the price of the graphics card itself, but also additional factors, such as driver availability, compatibility with existing infrastructure, and maintenance costs. For example, for corporate solutions where high reliability is required, Tesla A6000 may be more profitable compared to cheaper alternatives, such as A5000 ($2,500-3,000), if we consider reduced risks of failures and the need for frequent equipment replacement. Thus, the price-performance ratio requires careful analysis in the context of specific business goals, including product time-to-market and potential benefits from accelerating ML processes. Best Graphics Cards for AI in 2025 The GPU market in 2025 offers the best solutions for different budgets and tasks. Optimal Solutions for Beginners (under $1,000) For students and small businesses, the best NVIDIA graphic card for AI would be RTX 4060 Ti (16 GB, approximately $500). This graphics card will handle educational tasks excellently, such as data classification or small neural networks. RTX 4060 Ti provides high performance with 16 GB VRAM and Tensor Cores support. Alternative: AMD RX 6800 (16 GB, approximately $500) with ROCm for more complex projects. Case: A student trains a text analysis model on RTX 4060 Ti. Mid-Range: Balance of Power and Price NVIDIA A5000 (24 GB, approximately $3,000) is a universal choice for medium models and research. It's suitable for tasks like data analysis or content generation. Alternative: AMD Radeon Pro W6800 (32 GB, approximately $2,500) is a powerful competitor with increased VRAM and improved ROCm support, ideal for medium projects. Case: A media company uses A5000 for generative networks, accelerating video production by 35%. Professional Graphics Cards for Advanced Tasks Tesla A6000 (48 GB, approximately $5,000), Tesla H100 (80 GB, approximately $30,000), and Tesla H200 (141 GB, approximately $35,000) are great for large models and corporate tasks. Alternative: AMD MI300X (64 GB, approximately $20,000) is suitable for supercomputers, but inferior in ecosystem. Case: An AI startup trains a multimodal model on Tesla H200, reducing development time by 60%. NVIDIA vs AMD for AI NVIDIA remains the leader in ML, but AMD is actively catching up. The choice depends on budget, tasks, and ecosystem. Here's a comparison: Parameter NVIDIA AMD Ecosystem CUDA, wide support ROCm, limited VRAM 12-141 GB 16-64 GB Price More expensive Cheaper Tensor Cores Yes No Community Large Developing Why NVIDIA is the Choice of Most Developers NVIDIA dominates thanks to a wide range of advantages that make it preferred for developers and businesses worldwide: CUDA: This platform has become the de facto standard for ML, providing perfect compatibility with frameworks such as PyTorch, TensorFlow, and JAX. Libraries optimized for CUDA allow accelerating development and reducing costs for code adaptation. Tensor Cores: Specialized blocks that accelerate low-precision operations (FP16/FP32) provide a significant advantage when training modern neural networks, especially in tasks requiring high performance, such as generative AI. Energy Efficiency: The new Hopper architecture demonstrates outstanding performance-to-power consumption ratio, which reduces operating costs for data centers and companies striving for sustainable development. Community Support: A huge ecosystem of developers, documentation, and ready-made solutions simplifies the implementation of NVIDIA GPUs in projects, reducing time for training and debugging. Case: A retail company uses Tesla A100 for demand forecasting, reducing costs by 25% and improving forecast accuracy thanks to broad tool support and platform stability. AMD GPU Capabilities in 2025 AMD offers an alternative that attracts attention thanks to competitive characteristics and affordable cost: ROCm: The platform is actively developing, providing improved support for PyTorch and TensorFlow. In 2025, ROCm becomes more stable, although it still lags behind CUDA in speed and universality. Price: AMD GPUs, such as MI300X (approximately $20,000), are the best budget GPUs for AI, as they are significantly cheaper than NVIDIA counterparts. It makes them attractive for universities, research centers, and companies with limited budgets. Energy Efficiency: New AMD architectures demonstrate improvements in energy consumption, making them competitive in the long term. HPC Support: AMD cards are successfully used in high-performance computing, such as climate modeling, which expands their application beyond traditional ML. Case: A university uses MI300X for research, saving 30% of budget and supporting complex simulations thanks to high memory density. However, the limited ROCm ecosystem and smaller developer community may slow adoption and require additional optimization efforts. Local GPU vs Cloud Solutions Parameter Local GPU Cloud Control Full Limited Initial Costs High Low Scalability Limited High When to Use Local Hardware Local GPUs are suitable for permanent tasks where autonomy and full control over equipment are important. For example, the R&D department of a large company can use Tesla A6000 for long-term research, paying for itself within a year thanks to stable performance. Local graphics cards are especially useful if the business plans intensive daily GPU use, as this eliminates additional rental costs and allows optimizing infrastructure for specific needs. Case: A game development company trains models on local A6000s, avoiding cloud dependency. Additionally, local solutions allow configuring cooling and power consumption for specific conditions, which is important for data centers and server rooms with limited resources. However, this requires significant initial investments and regular maintenance, which may not be justified for small projects or periodic tasks. Pros and Cons of Cloud Solutions Cloud solutions for GPU usage are becoming a popular choice thanks to their flexibility and accessibility, especially for businesses seeking to optimize machine learning costs. Let's examine the key advantages and limitations to consider when choosing this approach. Pros: Scalability: You can add GPUs as tasks grow, which is ideal for companies with variable workloads. This allows quick adaptation to new projects without needing to purchase new equipment. Flexibility: Paying only for actual usage reduces financial risks, especially for startups or companies testing new AI solutions. For example, you can rent Tesla A100 for experiments without spending $20,000 on purchase. Access to Top GPUs: Cloud providers give access to cutting-edge models that aren't available for purchase in small volumes or require complex installation. Updates and Support: Cloud providers regularly update equipment and drivers, relieving businesses of the need to independently monitor technical condition. Cons: Internet Dependency: Stable connection is critical, and any interruptions can stop model training, which is unacceptable for projects with tight deadlines. Long-term Costs: With intensive use, rental can cost more than purchasing local GPU. Case: A startup tests models on a cloud server with Tesla H100, saving $30,000 on GPU purchase and quickly adapting to project changes. However, for long-term tasks, they plan to transition to local A6000s to reduce costs. Conclusion Choosing a graphics card for neural networks and ML in 2025 depends on your tasks. Beginners should choose NVIDIA RTX 4060 Ti, which will handle educational projects and basic models. For the mid-segment, A5000 is a good solution, especially if you work with generative models and more complex tasks. For business and large research, Tesla A6000 remains the optimal choice, providing high video memory volume and performance. NVIDIA provides the best graphic cards for AI and maintains leadership thanks to the CUDA ecosystem and specialized Tensor Cores. However, AMD is gradually strengthening its position, offering ROCm support and more affordable solutions, making the GPU market for ML and AI increasingly competitive.
30 September 2025 · 12 min to read

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