Knowledge

Training large-scale AI models is far beyond the capabilities of ordinary desktops. If you plan to run LLMs, process big data, or design custom deep learning solutions, you need to know which AI server can actually handle the workload. The choice isn’t just “pick something with an RTX”—the entire platform matters: GPU, CPU, RAM, storage, cooling, and scalability.

Already trained the model? Now it’s time to move it into production—fast, stable, and without bottlenecks. This is where AI inference comes in: the phase in which your model processes live data and returns results in real time. To make this effective, you need the right hardware: an inference server capable of sustaining heavy loads, with a GPU and CPU optimized not for training but for low-latency, high-throughput inference at scale.

A server room is more than just a space for rack cabinets and blinking LEDs. It is a critical point of IT infrastructure that must operate continuously—regardless of weather, power supply, or system failure. If you want to ensure your company’s data remains secure and your equipment operates stably according to standards, every detail counts—from air conditioning, through raised flooring and emergency power, to access control and HSE documentation.

Do you have more workloads but no way to expand your hardware? Server virtualization is a method to maximize the efficiency of your existing infrastructure—without purchasing new servers, rack cabinets, or costly hardware. When properly implemented, it increases flexibility, simplifies management, shortens deployment times, and significantly reduces IT infrastructure operating costs.

Without them, nothing works as it should. A system administrator is the person behind every service, server, and application in the company – often invisible, yet indispensable. They are responsible for IT infrastructure security, performance, availability, and scalability. Beyond the purely technical role, they act as a business partner, ensuring that data remains secure, systems run without interruptions, and the organization can safely expand.

SSD or HDD – seemingly a simple question, yet the answer depends on what you aim to achieve. In data centers, where every terabyte, millisecond, and watt count, storage choice directly translates into costs, performance, and data security.

Tired of overloaded servers that can’t keep up with your company’s growth? Instead of investing in another expensive “just-in-case” machine, it’s worth considering a natively scalable solution – a compute cluster.

AI is not only models and data - it is also heat. A lot of heat. As server compute density scales up, the demand for efficient, reliable, and scalable cooling grows in lockstep.

Want to speed up your server without spending a fortune on an all-flash array? A hybrid SSD and HDD sounds like a reasonable compromise – at least on paper. In practice, hybrid drives can surprise you not only with their price, but also with their administrative requirements, scenario-dependent performance and hidden costs.

R640, T330 and M420. You probably know how Dell EMC uses letters and numbers to name its PowerEdge server models. If not, we highly recommend you to read this article to understand these naming conventions and which generation the servers belong to.

Choosing the best server for a business can be a difficult process. With so many server types, processors, hard drive, and other options to choose from, it’s almost impossible to know where to begin. If you want to know how to setup a server for your business, this is right place.

NVMe drives are modern data carriers that use the PCI Express interface to communicate with a computer. They are much faster and more efficient than traditional SSD drives that use the SATA interface.

In this article, we will explain what server renewal involves and what are the advantages for an organization of using a recertified server over a new one.

In this article, you'll learn what an NVMe drive is, how it works, its benefits for servers, and how to choose the best NVMe drive for your server.

Many companies are moving their infrastructures to cloud services. Meanwhile, it is still On-Premise solutions that guarantee greater reliability, security and scalability. Overall, they are also quite a lot cheaper.

Attention! We are reporting a critical security issue that may impact your server.

Read this article and contact us if you have any questions.

The scale of DDoS attacks and their increasing complexity means that service operators are increasingly looking for solutions that give them full control over protection. See which solutions will be effective.

When it comes to servers, one mistake can turn everything upside down. Imagine a drive crashes, the system freezes, data disappears and the loss meter is ticking. So how do you protect against such scenarios?

Do you know how complex energy and power management can be in a Data Center ecosystem? It is often the key to optimizing, securing, and ensuring the continuity of the entire server infrastructure.

Find out what to do if you keep getting the message ‘DNS server not responding’. Learn about the most effective solutions to the problem and its causes.

Many administrators associate SNMP with ‘something that collects data from routers’. But before you set up your first agent and connect monitoring, it is worth understanding how this protocol works, what to expect from it – and what absolutely not to do.

In this post, you will learn what IOPS really means, how to measure it, when it matters, and why choosing a drive without this knowledge is simply a gamble.

TBW (Total Bytes Written) is an indicator that tells you how much data you can write to an SSD over its lifetime before the memory cells wear out to the point where the drive becomes unstable.

High Bandwidth Memory (HBM) is a DRAM technology designed for maximum bandwidth and minimal power consumption, used primarily in graphics chips and AI accelerators.

ECC or non-ECC RAM – this decision can affect the stability of the entire infrastructure, especially where data must not only be fast, but above all correct.

In enterprise environments, the concept of a computer network means much more than just connectivity between devices. It is the backbone of IT infrastructure, supporting communication between services, access to applications, access control, and security monitoring.

Choosing a processor for a server or workstation is not just about the number of cores. If you want to avoid costly mistakes, you need to understand what the symbols, numbers and letters in Intel CPU names really mean.

Remote access to the server, even when the system is down? IPMI makes it possible – without any tricks. This standard for management outside the operating system has been present in enterprise-class hardware for years, but it is still often underestimated.

Hardware Direct is proud to announce that we have become an authorized partner of Proxmox Server Solutions—the European leader in the field of virtualization.

Monday, 20 October 2025, will go down in history as the day when a significant part of the internet simply stopped working. All because of a failure at Amazon Web Services (AWS), the world's largest cloud service provider.

Learn why Proxmox VE is becoming a true alternative to VMware: full enterprise features with no license costs, flexible support subscriptions, high availability and significant savings when building your server infrastructure.

In common parlance, when users talk about controllers in active-active arrays, they mean that both controllers work simultaneously and handle I/O traffic at the same time. In practice, this is not always the case.

I would like to introduce you to the Dell PowerStore solution—a platform designed from the ground up as a foundation for modern data centers. As an experienced business advisor on these platforms, I see not just hardware, but a solution tailored to the modern era of infrastructure.

We are proud to announce that Hardware Direct has achieved Dell Technologies Gold Partner status, enabling us to deliver comprehensive IT infrastructure projects based on Dell’s most advanced technologies.

IT infrastructure planning requires balancing architecture, budget, and deployment schedules. In today’s fast-paced environment, procurement lead time is often the critical factor determining a project's success.

Building an in-house environment for training AI models and Machine Learning is a natural step toward technological independence for many companies. When designing such infrastructure, there is often a temptation to optimize costs by combining the latest GPU accelerators with proven, previous-generation servers. But is this always a safe solution?

Explore the key updates in Proxmox VE 9.2. See how Dynamic Load Balancer, SDN with WireGuard, and new cluster features enhance stability and automation.

Dell PowerEdge R7425 is one of those servers that looks "just solid" on paper, but when you see it with AI, that's where its real advantage becomes clear. Because here it's not just about the number of cores or CPU benchmark. This server was built for a very specific scenario: lots of GPUs, lots of NVMe and enormous data flow between them.

A few years ago, a recertified server was mainly associated with "cheaper used equipment". Today the situation looks completely different. With lead times for new GPUs measured in months and increasingly higher prices for DDR5 memory and AI accelerators, a well-configured Dell PowerEdge Recertified has become for many companies a normal way to quickly launch AI infrastructure.

A year ago, many companies assumed that local LLM was just a game for the biggest players. Today it's increasingly clear that a well-configured Dell R750xa or R760xa comfortably suffices for building a private AI environment – especially for fine-tuning, inference and working with your own data. And that's exactly why these servers started appearing not just in HPC, but also in regular IT departments.

C4140 and DSS8440 are no longer the "hottest" GPU platforms on the market – but that doesn't mean they've stopped making sense. Quite the opposite. In 2026 they can still be very powerful AI nodes, if the goal isn't breaking benchmark records, but building real HPC and AI infrastructure with a reasonable budget, high GPU density and sensible TCO.

With AI servers it's very easy to fall into thinking: "the more RAM, the better". The problem is that memory can today cost more than part of the GPU, and a poorly selected configuration quickly becomes a very expensive bottleneck.

When buying an AI server, it's very easy to conclude that the best approach is to go for the "most powerful possible configuration" right away. The problem is that a top GPU server costing hundreds of thousands of dollars won't always be the most cost-effective.

Image and video AI models can overwhelm a server much faster than classic text-based LLMs. Here the problem isn't just the model itself, but also thousands of frames, augmentations, preprocessing and gigantic data transfer between GPU, RAM and storage.

3D rendering can very quickly show whether infrastructure was built "to spec" or for real studio work. If several people simultaneously launch scenes in Blender, Unreal Engine or Redshift, it suddenly becomes clear that a regular workstation isn't enough.

More and more companies today reach a point where the classic "throw everything to the cloud" approach stops making sense. The problem becomes not just GPU costs, but also latency, data privacy, compliance and predictability of the entire AI environment. And that's exactly why the hybrid model – where training happens in the cloud and inference runs locally – is becoming for many organizations simply the most sensible AI architecture.

Choosing a server is not just a decision about what to buy today – but about how your company will operate for the next several years. Tower? Rack? Or something in between? In this guide you'll find concrete answers, comparisons, application examples and model recommendations tailored to various scenarios.

Rack or Blade – this is not just a question about the chassis, but about how your entire IT infrastructure operates. One will work well with steady expansion and limited budget. The other – with rapid scaling and large requirements. In this post you'll find concrete comparisons, numbers, scenarios and differences that really matter. All to select a solution tailored to your company's real needs, not trends.

The two most popular server systems on the market. Both proven. Both stable. Both have their supporters. But when it comes to choosing a specific model – it's no longer about the logo on the chassis. What matters is how the equipment performs in practice, how much it costs to maintain, how long it lasts without failure and whether you can expand it without headaches. In this text we compare Dell PowerEdge and HP ProLiant.

Rack servers are not just metal boxes for a cabinet – they're the backbone of many modern companies. At first glance they look similar, but they differ in everything: from dimensions and construction, through cooling methods, to how they're deployed and expanded. For some it's the first step toward order in IT infrastructure. For others – the standard without which ERP system, virtualization environment or database backup won't run.

Tower server is not a relic of the past – it's still a real, sensible option for companies that don't need an entire rack cabinet but want organized IT infrastructure. It looks like a larger desktop computer from the outside, but under the chassis hides enterprise-class solutions: RAID controllers, ECC RAM, redundant power supplies, remote management and virtualization support.

A backup server is not a "cheaper version" of a production server. It's a separate, capacious and resilient repository that must work precisely when everything else fails. If you're implementing Veeam or Nakivo, three things are key: capacity for retention, resilient RAID level and enterprise-class drives, and only then processor brand or core count. Backup server configuration starts with failure scenario – not price list.

The first server in a small company doesn't need to be complicated – it just needs to work stably, quietly and safely for a few years. In practice, for a team of 5-20 users it's about handling a few repeating roles: domain login, shared files, accounting software, sometimes a small SQL database and computer backups.

Virtualization entices with simplicity – "throw VMs on the host and it works". In practice it works only when cores, RAM and storage were calculated before you clicked the VMware installation. A server for multi-machine environment is not a "bigger computer". It's a platform meant to carry a dozen, sometimes dozens of systems simultaneously – with different load profiles, different memory appetite and different I/O needs.

A database doesn't forgive configuration mistakes – if hardware is poorly selected, the application starts to "choke", users wait for responses, and the IT department explains delays. In practice, an efficient SQL server is not a matter of "most expensive configuration", but conscious balance between RAM, storage, CPU and entire platform architecture. If you're planning ERP, CRM, warehouse system or reporting environment implementation – it's worth organizing this topic before purchase, not after first failure.

ERP system can run smoothly for years – provided it stands on a well-selected server, not a random configuration "because it suffices". In practice, most performance problems don't result from software itself, but from poor hardware decisions made at the start. Too few cores, memory selected "on a shoestring", slow disks or no headroom for growth – this is exactly where downtime, user frustration and IT department phone calls begin.

If you're facing server selection and see "CTO ready to ship", the natural question is: is this actually better than ready-made configuration, or just a marketing name. In practice it's about something very specific – you get hardware tailored to your application, but without waiting weeks. Let's break this down into real scenarios: when CTO makes sense, what configuration looks like and where people most often overpay.

If you've been doing AI longer than a moment, sooner or later you hit that point: GPU bills start looking seriously concerning. And then the question appears – keep running in cloud, or set up your own GPU server? Let's break this down into specifics: how much it costs, when it pays back and where it actually makes sense.

If you're facing choice: set up your own server or go cloud, it's not a "technical" decision at all – only business one. Because it's not just about performance, but costs, data control and how your company will operate in a year or two. In a moment you'll see where cloud makes sense and where local server wins without discussion.

If you reach moment when you wonder "should I keep pushing on workstation or move to server" – that means you're exactly at point where real AI infrastructure begins. And here decision really makes difference, because it affects not just performance but also how you'll work in 3-6 months.

If you're facing server selection and see "recertified", "refurbished" or just "used" in offers – it's easy to get lost. At first glance everything looks similar, and price differences can be significant. Problem is that in practice these are three completely different hardware levels and risks. Below we break it down – so it's clear what you're buying and whether it makes sense in your environment.

If you're facing server selection for AI, problem usually isn't "which model to choose" but how much power you really need. Because development and model testing is one thing, training large networks or working on multiple datasets simultaneously is another. In a moment we'll organize this simply – what matters, where not to burn budget and what configurations actually make sense in practice.

If you're thinking about AI on-premise, server choice doesn't start with Dell or HPE model, but with what you really want to do on it. Because hardware for inference, development and training large models are three completely different worlds. Below you have ranking for 2026 – divided by applications. So you know what makes sense in your case and where you can easily burn budget.

If you're planning AI project and counting on quick GPU server purchase – in 2026 you might be in for surprise. Graphics card availability is limited, and real delivery times can reach a year. This changes approach: instead of "buy equipment and deploy", you need to think like investment – what to do now, where to start and where not to burn budget.

AI server prices are rising faster than most companies can realistically grasp – and this applies even to identical configurations. Same Dell PowerEdge R750xa or R7425, with same CPU, RAM and GPU, can cost dozens percent more today than year or two ago.

No GPU access for next 12 months? That's not technical problem – only decision how you want to run project. Because today company with best equipment doesn't win, but one that can launch AI faster than others, even on less ideal infrastructure. And this is where real options start – not "textbook" ones, but ones that work in practice.

Most errors when buying server don't come from model choice, but how that equipment is financed. Because exact same Dell PowerEdge R760 can be excellent decision… or complete budget waste – depending on whether you buy it for cash, put it in lease, or treat as temporary rental.

Memory cost in servers in 2026 stopped being detail – it's one of main budget components. If planning upgrade or new hardware purchase, it's not CPU or chassis making biggest difference today, but DDR5 and SSD that within months managed to double their price. And that's not temporary fluctuation – just change affecting how much you really pay for every IT project.

With GPU number in server it's easy to go wrong right at start. Don't begin with "how many cards fit" but "what exactly do you want to compute and in what timeframe". And that makes huge difference because in practice one card can do job someone tries solving with four – just because configuration wasn't matched to model.

Choice between Dell Precision and Lenovo ThinkStation rarely comes down to "what's better". It's choice between performance and work comfort, between maximum power and lower energy consumption. And only when you look at concrete numbers does it get interesting – because differences aren't cosmetic.

Choice between Dell Precision and Lenovo ThinkStation rarely comes down to "what's better". It's choice between performance and work comfort, between maximum power and lower energy consumption. And only when you look at concrete numbers does it get interesting – because differences aren't cosmetic.

If wondering whether to put SSD or NVMe in AI server – answer is: depends on what you really do on disk. Not on GPU model, not on server price, only on whether your workloads actually "choke" storage. Because in many AI projects disk is background. In others – becomes bottleneck killing entire environment performance.

If facing AI server choice, purchase price alone tells little. Real cost emerges after 2-3 years when you add electricity, service, downtime and management. And then often decision "cheaper vs more expensive" looks completely different. Shortly we'll break this down to concrete numbers – so you can calculate it yourself, not guess.