{"id":17019,"date":"2026-05-25T09:00:12","date_gmt":"2026-05-25T08:00:12","guid":{"rendered":"https:\/\/ibertronica.es\/blog\/?p=17019"},"modified":"2026-05-22T08:13:51","modified_gmt":"2026-05-22T07:13:51","slug":"workstations-for-artificial-intelligence-and-deep-learning-a-complete-guide-2026","status":"publish","type":"post","link":"https:\/\/ibertronica.es\/blog\/en\/news-en\/workstations-for-artificial-intelligence-and-deep-learning-a-complete-guide-2026\/","title":{"rendered":"Workstations for Artificial Intelligence and Deep Learning: a complete guide 2026"},"content":{"rendered":"<h1>Workstation for Artificial Intelligence: Complete Guide to Choosing AI Hardware in 2026<\/h1>\n<p><strong>Artificial intelligence<\/strong> is no longer exclusive to large laboratories or hyperscalers. In 2026, more and more companies, universities, engineering studios, and development teams need to run AI models locally, securely, and with high performance. This is where the <strong>workstation for artificial intelligence<\/strong> comes into play.<\/p>\n<p>At Ibertr\u00f3nica, we have spent decades configuring high-performance systems for professionals. The difference today is that requirements have changed: training LLM models, running local inference, working with computer vision, or developing machine learning solutions requires hardware that is very different from a conventional PC.<\/p>\n<p>In this guide, we explain what an AI-oriented workstation should include, which components really matter, and which configurations we recommend depending on the type of project.<\/p>\n<h2>What Makes an AI Workstation Different from a Conventional PC<\/h2>\n<p>A <strong>workstation for artificial intelligence<\/strong> is designed to handle intensive and sustained workloads for hours or even days. It is not simply \u201ca powerful computer\u201d.<\/p>\n<p>The main differences compared to a conventional PC are:<\/p>\n<ul>\n<li>Professional GPUs with a large amount of VRAM.<\/li>\n<li>Greater thermal and electrical stability.<\/li>\n<li>Ability to work with multiple GPUs.<\/li>\n<li>High-capacity RAM.<\/li>\n<li>High-performance NVMe storage for datasets.<\/li>\n<li>Certified compatibility with professional software.<\/li>\n<li>Chassis and power supplies prepared for continuous workloads.<\/li>\n<\/ul>\n<p>While a home computer can run basic AI tasks or small models, a professional workstation allows you to:<\/p>\n<ul>\n<li>Train deep learning models.<\/li>\n<li>Run LLMs locally.<\/li>\n<li>Fine-tune models.<\/li>\n<li>Process large datasets.<\/li>\n<li>Work with real-time computer vision.<\/li>\n<li>Render and generate content using AI.<\/li>\n<\/ul>\n<p>In business environments, factors such as <strong>reliability<\/strong>, <strong>technical support<\/strong>, <strong>scalability<\/strong>, and <strong>operational continuity<\/strong> also come into play.<\/p>\n<p><a href=\"https:\/\/ibertronica.es\/equipos\/workstations\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-17016 size-full\" title=\"Workstations Para Inteligencia Artificial 3\" src=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-3.jpg\" alt=\"Workstations for Artificial Intelligence 3\" width=\"1000\" height=\"571\" srcset=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-3.jpg 1000w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-3-300x171.jpg 300w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-3-150x86.jpg 150w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-3-203x116.jpg 203w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/a><\/p>\n<h2>Training vs Inference: Hardware Implications<\/h2>\n<p>One of the most common mistakes is thinking that all AI workloads require the same type of hardware.<\/p>\n<p>It is not the same to:<\/p>\n<ul>\n<li>Train a model from scratch.<\/li>\n<li>Perform fine-tuning.<\/li>\n<li>Run inference.<\/li>\n<li>Serve models in production.<\/li>\n<\/ul>\n<h3>Training<\/h3>\n<p>Training is the most demanding phase. Here, the <strong>GPU<\/strong> is absolutely critical.<\/p>\n<p>The most important factors are:<\/p>\n<ul>\n<li>Available VRAM.<\/li>\n<li>Memory bandwidth.<\/li>\n<li>Tensor performance.<\/li>\n<li>Multi-GPU capability.<\/li>\n<li>Stable cooling.<\/li>\n<\/ul>\n<p>Modern models such as <strong>Llama<\/strong>, <strong>Mistral<\/strong>, or <strong>Mixtral<\/strong> can consume huge amounts of memory, even in fine-tuning tasks.<\/p>\n<p>For serious training, it is now common to work with:<\/p>\n<ul>\n<li>NVIDIA H100.<\/li>\n<li>NVIDIA H200.<\/li>\n<li>NVIDIA RTX PRO 6000 Blackwell.<\/li>\n<li>HGX infrastructure.<\/li>\n<\/ul>\n<h3>Inference<\/h3>\n<p>Inference has different requirements.<\/p>\n<p>Here, the most important aspects are usually:<\/p>\n<ul>\n<li>Latency.<\/li>\n<li>Power consumption.<\/li>\n<li>Tokens per second.<\/li>\n<li>Performance\/price ratio.<\/li>\n<\/ul>\n<p>Many companies run local inference with professional RTX GPUs or even advanced gaming GPUs when the budget is more limited.<\/p>\n<h3>Fine-Tuning<\/h3>\n<p>Fine-tuning sits somewhere in between.<\/p>\n<p>With techniques such as:<\/p>\n<ul>\n<li>LoRA.<\/li>\n<li>QLoRA.<\/li>\n<li>Quantization.<\/li>\n<\/ul>\n<p>It is possible to adapt large models using more affordable hardware.<\/p>\n<p>That is why many companies choose workstations with <strong>RTX PRO 6000 Blackwell<\/strong> or multi-GPU configurations before making the leap to large-scale GPU servers.<\/p>\n<h2>GPU: The Critical Component<\/h2>\n<p>If there is one component that defines an <strong>AI workstation<\/strong>, it is the GPU.<\/p>\n<p>The difference between a functional machine and an insufficient one is usually found here.<\/p>\n<h3>VRAM: The Most Important Factor<\/h3>\n<p><strong>VRAM<\/strong> determines the size of the model and batch you will be able to handle.<\/p>\n<p>As a general reference:<\/p>\n<table style=\"width: 100%;\" border=\"0\" cellspacing=\"1\" cellpadding=\"1\">\n<tbody>\n<tr>\n<td><strong>Use Case<\/strong><\/td>\n<td><strong>Recommended VRAM<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Basic LLM 7B inference<\/td>\n<td>16-24 GB<\/td>\n<\/tr>\n<tr>\n<td>Fine-tuning 7B-13B models<\/td>\n<td>24-48 GB<\/td>\n<\/tr>\n<tr>\n<td>Advanced training<\/td>\n<td>80-141 GB<\/td>\n<\/tr>\n<tr>\n<td>Enterprise multi-GPU<\/td>\n<td>160 GB+<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>When the model does not fit into memory:<\/p>\n<ul>\n<li>Performance drops dramatically.<\/li>\n<li>Bottlenecks appear.<\/li>\n<li>Training may become unfeasible.<\/li>\n<\/ul>\n<h3>Bandwidth and HBM Memory<\/h3>\n<p>The amount of memory is not the only important factor. <strong>Bandwidth<\/strong> is key in AI.<\/p>\n<p>The <strong>H100<\/strong> and <strong>H200<\/strong> GPUs stand out precisely because they use HBM3 and HBM3e memory with several TB\/s of bandwidth.<\/p>\n<p>This greatly accelerates:<\/p>\n<ul>\n<li>Distributed training.<\/li>\n<li>Attention in LLMs.<\/li>\n<li>Batch inference.<\/li>\n<li>Tensor processing.<\/li>\n<\/ul>\n<h3>CUDA and the NVIDIA Ecosystem<\/h3>\n<p>In 2026, <strong>NVIDIA<\/strong> continues to clearly dominate the professional AI ecosystem.<\/p>\n<p>Most frameworks are optimized for:<\/p>\n<ul>\n<li>CUDA.<\/li>\n<li>cuDNN.<\/li>\n<li>TensorRT.<\/li>\n<li>NCCL.<\/li>\n<\/ul>\n<p>And platforms such as:<\/p>\n<ul>\n<li>PyTorch.<\/li>\n<li>TensorFlow.<\/li>\n<li>JAX.<\/li>\n<\/ul>\n<p>They perform better and receive priority support in NVIDIA environments. That is why most professional AI workstations continue to be based on NVIDIA GPUs.<\/p>\n<h2>CPU and RAM: How Much Do You Really Need?<\/h2>\n<p>Although the GPU is the main component, <strong>CPU<\/strong> and <strong>RAM<\/strong> remain essential.<\/p>\n<h3>CPU<\/h3>\n<p>The CPU handles:<\/p>\n<ul>\n<li>Dataset preparation.<\/li>\n<li>Compression.<\/li>\n<li>Pipelines.<\/li>\n<li>Orchestration.<\/li>\n<li>Virtualization.<\/li>\n<li>Compilation.<\/li>\n<\/ul>\n<p>The most common platforms for AI workstations are:<\/p>\n<ul>\n<li>AMD Threadripper PRO.<\/li>\n<li>Intel Xeon W.<\/li>\n<\/ul>\n<h3>How Many Cores Do You Need?<\/h3>\n<p>It depends on the workflow:<\/p>\n<table style=\"width: 100%;\" border=\"0\" cellspacing=\"1\" cellpadding=\"1\">\n<tbody>\n<tr>\n<td><strong>Type of Use<\/strong><\/td>\n<td><strong>Recommended CPU<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Light AI \/ inference<\/td>\n<td>16-24 cores<\/td>\n<\/tr>\n<tr>\n<td>Fine-tuning and development<\/td>\n<td>24-64 cores<\/td>\n<\/tr>\n<tr>\n<td>Multi-GPU \/ virtualization<\/td>\n<td>64-96 cores<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>RAM Memory<\/h3>\n<p>RAM usually runs out sooner than expected.<\/p>\n<p>Realistic minimum recommendation in 2026:<\/p>\n<table style=\"width: 100%;\" border=\"0\" cellspacing=\"1\" cellpadding=\"1\">\n<tbody>\n<tr>\n<td><strong>Scenario<\/strong><\/td>\n<td><strong>Recommended RAM<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Basic AI development<\/td>\n<td>64 GB<\/td>\n<\/tr>\n<tr>\n<td>Professional AI workstation<\/td>\n<td>128 GB<\/td>\n<\/tr>\n<tr>\n<td>Large datasets<\/td>\n<td>256-512 GB<\/td>\n<\/tr>\n<tr>\n<td>HPC \/ multi-user<\/td>\n<td>1 TB+<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>Many modern AI workflows load huge volumes of data into memory to speed up training. That is why <strong>128 GB<\/strong> has practically become the professional entry point.<\/p>\n<h2>Storage for Datasets: NVMe, Tiers, and Capacity<\/h2>\n<p>Another common mistake is underestimating storage.<\/p>\n<p>Modern datasets can take up:<\/p>\n<ul>\n<li>Hundreds of GB.<\/li>\n<li>Several TB.<\/li>\n<li>Tens of TB in enterprise environments.<\/li>\n<\/ul>\n<h3>NVMe PCIe Gen4 and Gen5<\/h3>\n<p>For AI, <strong>NVMe<\/strong> storage is mandatory.<\/p>\n<p>Traditional SATA SSDs create major bottlenecks.<\/p>\n<p>We currently recommend:<\/p>\n<ul>\n<li>NVMe Gen4 as a minimum.<\/li>\n<li>Gen5 for intensive workloads.<\/li>\n<li>RAID configurations in advanced environments.<\/li>\n<\/ul>\n<h3>Tiered Storage Strategy<\/h3>\n<p>Many companies implement:<\/p>\n<table style=\"width: 100%;\" border=\"0\" cellspacing=\"1\" cellpadding=\"1\">\n<tbody>\n<tr>\n<td><strong>Tier<\/strong><\/td>\n<td><strong>Function<\/strong><\/td>\n<\/tr>\n<tr>\n<td>Ultra-fast NVMe<\/td>\n<td>Active datasets<\/td>\n<\/tr>\n<tr>\n<td>Secondary SSDs<\/td>\n<td>Projects and cache<\/td>\n<\/tr>\n<tr>\n<td>NAS \/ server<\/td>\n<td>Archiving and backup<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<p>This allows companies to optimize both cost and performance.<\/p>\n<h2>Cooling and Power Consumption: Key Points Nobody Explains<\/h2>\n<p>Modern AI workstations consume much more power than traditional workstations.<\/p>\n<p>A single advanced GPU can exceed:<\/p>\n<ul>\n<li>500W.<\/li>\n<li>600W.<\/li>\n<li>Even more under sustained workloads.<\/li>\n<\/ul>\n<p>And a multi-GPU configuration can easily approach:<\/p>\n<ul>\n<li>2000W.<\/li>\n<li>3000W.<\/li>\n<\/ul>\n<h3>Cooling<\/h3>\n<p>This is where many problems appear in poorly designed configurations:<\/p>\n<ul>\n<li>Thermal throttling.<\/li>\n<li>Excessive noise.<\/li>\n<li>Instability.<\/li>\n<li>Reduced lifespan.<\/li>\n<\/ul>\n<p>That is why the following are essential:<\/p>\n<ul>\n<li>Professional airflow.<\/li>\n<li>AI-ready chassis.<\/li>\n<li>Platinum\/Titanium power supplies.<\/li>\n<li>Optimized cooling.<\/li>\n<li>Correct GPU distribution.<\/li>\n<\/ul>\n<p>At Ibertr\u00f3nica, we design configurations specifically validated for continuous AI workloads.<\/p>\n<h2>3 Recommended Configurations in 2026<\/h2>\n<h3>1. Entry-Level Local AI<\/h3>\n<p><strong>Recommended Profile<\/strong><\/p>\n<ul>\n<li>AI development.<\/li>\n<li>Local inference.<\/li>\n<li>LLM 7B-13B.<\/li>\n<li>Business automation.<\/li>\n<\/ul>\n<p><strong>Suggested Configuration<\/strong><\/p>\n<ul>\n<li>AMD Threadripper CPU.<\/li>\n<li>64-128 GB RAM.<\/li>\n<li>NVIDIA RTX 5080 \/ RTX PRO.<\/li>\n<li>2 TB NVMe.<\/li>\n<\/ul>\n<p><strong>Approximate budget:<\/strong> between \u20ac3,500 and \u20ac6,000.<\/p>\n<h3>2. Professional Research and Fine-Tuning<\/h3>\n<p><strong>Recommended Profile<\/strong><\/p>\n<ul>\n<li>Universities.<\/li>\n<li>AI startups.<\/li>\n<li>ML teams.<\/li>\n<li>LLM fine-tuning.<\/li>\n<\/ul>\n<p><strong>Suggested Configuration<\/strong><\/p>\n<ul>\n<li>Threadripper PRO.<\/li>\n<li>128-256 GB RAM.<\/li>\n<li>RTX PRO 6000 Blackwell.<\/li>\n<li>4-8 TB NVMe.<\/li>\n<\/ul>\n<p><strong>Approximate budget:<\/strong> between \u20ac10,000 and \u20ac20,000.<\/p>\n<h3>3. Advanced Multi-GPU Training<\/h3>\n<p><strong>Recommended Profile<\/strong><\/p>\n<ul>\n<li>Large models.<\/li>\n<li>HPC.<\/li>\n<li>Enterprise AI.<\/li>\n<li>Massive computer vision.<\/li>\n<\/ul>\n<p><strong>Suggested Configuration<\/strong><\/p>\n<ul>\n<li>Dual CPU or advanced Threadripper PRO.<\/li>\n<li>512 GB-1 TB RAM.<\/li>\n<li>H100\/H200 multi-GPU.<\/li>\n<li>Enterprise NVMe infrastructure.<\/li>\n<\/ul>\n<p><strong>Approximate budget:<\/strong> from \u20ac40,000.<\/p>\n<p><strong>Looking for a professional workstation for AI?<\/strong> At Ibertr\u00f3nica, we configure custom systems for local inference, fine-tuning, ML development, and advanced GPU workloads.<\/p>\n<p><a href=\"https:\/\/ibertronica.es\/equipos\/workstations\"><strong>View professional workstations<\/strong><\/a><\/p>\n<p><a href=\"https:\/\/ibertronica.es\/equipos\/workstations\"><img loading=\"lazy\" decoding=\"async\" class=\"aligncenter wp-image-17017 size-full\" title=\"Workstations Para Inteligencia Artificial 2\" src=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-2.jpg\" alt=\"Workstations for Artificial Intelligence 2\" width=\"1000\" height=\"571\" srcset=\"https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-2.jpg 1000w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-2-300x171.jpg 300w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-2-150x86.jpg 150w, https:\/\/ibertronica.es\/blog\/wp-content\/uploads\/2026\/05\/Workstations-para-Inteligencia-Artificial-2-203x116.jpg 203w\" sizes=\"auto, (max-width: 1000px) 100vw, 1000px\" \/><\/a><\/p>\n<h2>Software Stack: CUDA, Frameworks, and Drivers<\/h2>\n<p>Hardware is only one part of the ecosystem. <strong>Software compatibility<\/strong> is critical.<\/p>\n<h3>Most Commonly Used Frameworks<\/h3>\n<p>The most common environments today are:<\/p>\n<ul>\n<li>PyTorch.<\/li>\n<li>TensorFlow.<\/li>\n<li>JAX.<\/li>\n<li>ONNX.<\/li>\n<li>TensorRT.<\/li>\n<li>CUDA Toolkit.<\/li>\n<\/ul>\n<h3>Linux vs Windows<\/h3>\n<p>In professional AI, <strong>Linux<\/strong> clearly dominates.<\/p>\n<p>Especially:<\/p>\n<ul>\n<li>Ubuntu.<\/li>\n<li>Rocky Linux.<\/li>\n<li>Debian.<\/li>\n<\/ul>\n<p>Although some companies continue to use Windows for hybrid AI, CAD, and rendering workflows.<\/p>\n<h3>Certified Drivers<\/h3>\n<p>One of the main strengths of professional GPUs is precisely driver stability.<\/p>\n<p>In production environments, this makes a big difference compared to gaming hardware.<\/p>\n<h2>Workstation vs GPU Server: When to Choose Each One<\/h2>\n<p>Not every project needs a GPU cluster.<\/p>\n<h3>When to Choose a Workstation<\/h3>\n<p>A workstation is usually ideal when:<\/p>\n<ul>\n<li>The team is small.<\/li>\n<li>Development is local.<\/li>\n<li>Flexibility is required.<\/li>\n<li>The budget is limited.<\/li>\n<li>AI is combined with other professional tasks.<\/li>\n<\/ul>\n<h3>When to Choose a GPU Server<\/h3>\n<p>A GPU server starts to make sense when:<\/p>\n<ul>\n<li>There are multiple users.<\/li>\n<li>High availability is required.<\/li>\n<li>Distributed training is needed.<\/li>\n<li>Dozens of GPUs are required.<\/li>\n<li>There are enterprise deployments.<\/li>\n<\/ul>\n<p>Many companies start with workstations and later evolve towards dedicated GPU servers.<\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>How Much VRAM Do I Need to Work with AI?<\/h3>\n<p>It depends on the model. For modern LLMs, <strong>24 GB<\/strong> is usually the practical professional minimum.<\/p>\n<h3>Is a Gaming GPU or a Professional GPU Better?<\/h3>\n<p>For business projects and continuous workloads, professional GPUs offer better stability, certified drivers, and greater memory capacity.<\/p>\n<h3>Can I Run LLMs Locally?<\/h3>\n<p>Yes. Today, it is perfectly feasible to run 7B, 13B, and even larger models on modern workstations.<\/p>\n<h3>How Much RAM Do I Really Need?<\/h3>\n<p>For professional AI, we recommend a minimum of <strong>128 GB<\/strong>.<\/p>\n<h3>Linux or Windows?<\/h3>\n<p>Linux remains the preferred option in professional AI due to compatibility and performance.<\/p>\n<h2>Conclusion<\/h2>\n<p>Choosing the right <strong>workstation for artificial intelligence<\/strong> can make a huge difference in productivity, scalability, and operating costs.<\/p>\n<p>In modern AI, hardware matters. A lot.<\/p>\n<p>The key is to design a balanced platform:<\/p>\n<ul>\n<li>Suitable GPU.<\/li>\n<li>Enough memory.<\/li>\n<li>Stable cooling.<\/li>\n<li>Fast storage.<\/li>\n<li>Future scalability.<\/li>\n<\/ul>\n<p>At Ibertr\u00f3nica, we help companies, universities, and professionals configure <strong>AI workstations<\/strong> adapted to each real need.<\/p>\n<p>We do not work with generic configurations: we design solutions optimized for each workload.<\/p>\n<p><strong>Not sure what hardware you need for your AI project?<\/strong> Our technical team can help you define the most suitable GPU, CPU, RAM, storage, and cooling according to your workload.<\/p>\n<p><a onclick=\"javascript:pageTracker._trackPageview('\/mailto\/comercial@ibertronica.es');\"  href=\"mailto:comercial@ibertronica.es\"><strong>Request personalized advice for your AI workstation<\/strong><\/a><\/p>\n<h2>Request Personalized Advice<\/h2>\n<p>If you are considering implementing local AI, training models, or deploying professional GPU infrastructure, our team can help you define the most suitable configuration.<\/p>\n<p>Request a custom quote for your <strong>workstation for artificial intelligence<\/strong>, and we will advise you according to:<\/p>\n<ul>\n<li>Type of models.<\/li>\n<li>Budget.<\/li>\n<li>Scalability.<\/li>\n<li>Power consumption.<\/li>\n<li>Number of users.<\/li>\n<li>Business goals.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Workstation for Artificial Intelligence: Complete Guide to Choosing AI Hardware in 2026 Artificial intelligence is no longer exclusive to large laboratories or hyperscalers. In 2026, more and more companies, universities, engineering studios, and development teams need to run AI models locally, securely, and with high performance. This is where the workstation for artificial intelligence comes into play. At Ibertr\u00f3nica, we have spent decades configuring high-performance systems for professionals. The difference today is that requirements have changed: training LLM models, running&hellip;<\/p>\n","protected":false},"author":2,"featured_media":17015,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1410],"tags":[2866,5000],"class_list":["post-17019","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-news-en","tag-artificial-intelligence","tag-workstation-en","post-has-thumbnail"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.1 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Workstation for Artificial Intelligence: AI Hardware Guide 2026- Blog de tecnolog\u00eda<\/title>\n<meta name=\"description\" content=\"Discover how to choose a workstation for artificial intelligence and deep learning in 2026. 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