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AI PC Buying Guide: Which AI PC Is Right for Your Project?
AI PC Buying Guide
Find the Right AI PC for You
AI PC Buying Guide
Whether an AI PC is worth the money depends on how well it suits its intended task. A language model that doesn’t fit entirely into the graphics memory can’t even be loaded in the first place. A training system with too little RAM spends more time reloading data than performing computations. And a quad-GPU system will be underutilized if it’s only used occasionally to generate images. This AI PC buying guide helps you make an informed choice: We’ll first advise you on the use case, then on the graphics card, and finally on the processor, RAM, and software. By the end, you’ll know which profile suits your project and what matters most when configuring your system.
Three Questions That Determine Your Choice of AI PC
Before we get into graphics cards and memory sizes, it’s worth taking a step back. Three questions will quickly narrow down your options.
1. What use case will run on the AI PC?
Inference using a pre-trained language model, image and video generation, developing your own models, and training from scratch place completely different demands on the hardware. Our AI workstation profiles are structured precisely around these four use cases. |
2. Which technical specifications are particularly important for my use case?
Graphics memory determines which model sizes can run at all. GPU computing power determines how quickly training and generation occur. The processor and RAM determine how quickly datasets are processed and reloaded. Choosing the right hardware before making a purchase will save you from having to upgrade at a later date. |
3. What is the maximum amount of hardware required for my project?
For inference and most development tasks, a single, adequately equipped graphics card is sufficient. Multiple GPUs are only worthwhile if you’re training your own models or if a model is so large that it can only be loaded across the memory of multiple cards. An example illustrates the difference. A team that wants to run a medium-sized language model as an internal assistant can get by with a single, well-equipped graphics card and 64 GB of RAM. A research team retraining a large model with its own data, on the other hand, will quickly end up needing several RTX PRO 6000s and significantly more RAM. Both work with language models yet require completely different systems. |
AI PC or Regular PC: When Does Specialized Hardware Pay Off?
On paper, a powerful desktop PC and an AI PC look similar. In some cases, they use the same CPU platforms with a similar number of cores, RAM, and storage. The difference lies in the priorities, because an AI PC is consistently designed for graphics memory and GPU throughput rather than clock speed and single-core performance. Four features clearly highlight the differences. The graphics card often comes with 24 GB or more of graphics memory—significantly more than is typical for graphics or video work—because the model and intermediate results must fit entirely within the VRAM. The platform offers enough PCIe lanes to run two to four graphics cards simultaneously at full bandwidth, for example, based on AMD Threadripper PRO or Intel Xeon. With up to 768 GB of RAM, the system’s memory capacity far exceeds the needs of creative workflows, ensuring that large datasets can be fully loaded into memory prior to training. And the software environment—including CUDA, PyTorch, and TensorFlow—can be preconfigured and optimized at the factory upon request.
If
you only run a pre-trained model locally from time to time, a single consumer graphics card in a standard PC is often a more cost-effective option. The math changes as soon as you train regularly, work with large datasets, or want to utilize multiple GPUs. From that point on, every hour saved in training directly frees up working time.
AI PC or Standard PC: When Does Specialized Hardware Pay Off?
On paper, a powerful desktop PC and an AI PC look similar. In some cases, they use the same CPU platforms with a similar number of cores, RAM, and storage. The difference lies in the priorities, because an AI PC is consistently designed for graphics memory and GPU throughput rather than clock speed and single-core performance. Four features clearly highlight the differences. The graphics card often comes with 24 GB or more of graphics memory—significantly more than is typical for graphics or video work—because the model and intermediate results must fit entirely within the VRAM. The platform offers enough PCIe lanes to run two to four graphics cards simultaneously at full bandwidth, for example, based on AMD Threadripper PRO or Intel Xeon. With up to 768 GB of RAM, the system’s memory capacity far exceeds the needs of creative workflows, ensuring that large datasets can be fully loaded into memory prior to training. And the software environment—including CUDA, PyTorch, and TensorFlow—can be preconfigured and optimized at the factory upon request.
If you only run a pre-trained model locally from time to time, a single consumer graphics card in a standard PC is often a more cost-effective option. The math changes as soon as you train regularly, work with large datasets, or want to utilize multiple GPUs. From that point on, every hour saved in training directly frees up working time.
AI PC or Cloud: When Local AI Infrastructure Pays Off
More and more AI workloads are migrating from the cloud to on-premises systems, and there are three reasons for this. The first is data privacy. On an on-premises AI PC, customer documents, source code, and research data never leave your own premises, which significantly simplifies compliance issues. The second reason is cost. Cloud GPUs and API calls are billed based on usage; with daily work, your own hardware often pays for itself within a year, and after that, every additional GPU hour effectively costs only electricity. The third reason is availability. Your hardware is exclusively at your disposal at all times, without having to wait in line for available instances and without latency to external infrastructure. The cloud remains the go-to option for rare, very large training runs that would overwhelm even a four-GPU system. Many teams therefore take a two-pronged approach, handling development, inference, and day-to-day operations locally on the AI PC, while the few large-scale training runs are performed in the cloud.
Whether an AI PC is worth the money depends on how well it suits its intended task. A language model that doesn’t fit entirely into the graphics memory can’t even be loaded in the first place. A training system with too little RAM spends more time reloading data than performing computations. And a quad-GPU system will be underutilized if it’s only used occasionally to generate images. This AI PC buying guide helps you sort through your options: We’ll first advise you on the use case, then on the graphics card, and finally on the processor, RAM, and software. By the end, you’ll know which profile suits your project and what matters most when configuring your system.
Three Questions That Determine Your Choice of AI PC
Before we get into graphics cards and memory sizes, it’s worth taking a step back. Three questions will quickly narrow down your options.
1. What use case will run on the AI PC?
Inference using a pre-trained language model, image and video generation, developing your own models, and training from scratch place completely different demands on the hardware. Our AI workstation profiles are structured precisely around these four use cases. |
2. Which technical specifications are particularly important for my use case?
Graphics memory determines which model sizes can run at all. GPU computing power determines how quickly training and generation occur. The processor and RAM determine how quickly datasets are processed and reloaded. Choosing the right hardware before making a purchase will save you from having to upgrade at a later date. |
3. What is the maximum amount of hardware required for my project?
For inference and most development tasks, a single, well-equipped graphics card is sufficient. Multiple GPUs are only worthwhile if you’re training your own models or if a model is so large that it can only be loaded across the memory of multiple cards. An example illustrates the difference. A team that wants to run a medium-sized language model as an internal assistant can get by with a single, well-equipped graphics card and 64 GB of RAM. A research team retraining a large model with its own data, on the other hand, will quickly end up needing several RTX PRO 6000s and significantly more RAM. Both work with language models yet require completely different systems. |
AI PC or Regular PC: When Does Specialized Hardware Pay Off?
On paper, a powerful desktop PC and an AI PC look similar. In some cases, they use the same CPU platforms with a similar number of cores, RAM, and storage. The difference lies in the priorities, because an AI PC is consistently designed for graphics memory and GPU throughput rather than clock speed and single-core performance. Four features clearly highlight the differences. The graphics card often comes with 24 GB or more of graphics memory—significantly more than is typical for graphics or video work—because the model and intermediate results must fit entirely within the VRAM. The platform offers enough PCIe lanes to run two to four graphics cards simultaneously at full bandwidth, for example, based on AMD Threadripper PRO or Intel Xeon. With up to 768 GB of RAM, the system’s memory capacity far exceeds the needs of creative workflows, ensuring that large datasets can be fully loaded into memory prior to training. And the software environment—including CUDA, PyTorch, and TensorFlow—can be preconfigured and optimized out of the box upon request.
If you only run a pre-trained model locally from time to time, a single consumer graphics card in a standard PC is often a more cost-effective option. The math changes as soon as you train regularly, work with large datasets, or want to utilize multiple GPUs. From that point on, every hour saved in training directly frees up work time.
AI PC or Standard PC: When Does Specialized Hardware Pay Off?
On paper, a powerful desktop PC and an AI PC look similar. In some cases, they use the same CPU platforms with a similar number of cores, RAM, and storage. The difference lies in the priorities, because an AI PC is consistently designed for graphics memory and GPU throughput rather than clock speed and single-core performance. Four features clearly highlight the differences. The graphics card often comes with 24 GB or more of graphics memory—significantly more than is typical for graphics or video work—because the model and intermediate results must fit entirely within the VRAM. The platform offers enough PCIe lanes to run two to four graphics cards simultaneously at full bandwidth, for example, based on AMD Threadripper PRO or Intel Xeon. With up to 768 GB of RAM, the system’s memory capacity far exceeds the needs of creative workflows, ensuring that large datasets can be fully loaded into memory prior to training. And the software environment—including CUDA, PyTorch, and TensorFlow—can be preconfigured and optimized out of the box upon request.
If you only run a pre-trained model locally from time to time, a single consumer graphics card in a standard PC is often a more cost-effective option. The math changes as soon as you train regularly, work with large datasets, or want to utilize multiple GPUs. From that point on, every hour saved in training directly frees up working time.
AI PC or Cloud: When Local AI Infrastructure Pays Off
More and more AI workloads are migrating from the cloud to on-premises systems, and there are three reasons for this. The first is data protection. On an on-premises AI PC, customer documents, source code, and research data never leave your own premises, which significantly simplifies compliance issues. The second reason is cost. Cloud GPUs and API calls are billed based on usage; with daily work, your own hardware often pays for itself within a year, and after that, every additional GPU hour effectively costs only electricity. The third reason is availability. Your hardware is exclusively at your disposal at all times, without having to wait in line for available instances and without latency to external infrastructure. The cloud remains the go-to option for rare, very large training runs that would overwhelm even a four-GPU system. Many teams therefore take a two-pronged approach, handling development, inference, and day-to-day operations locally on the AI PC, while the few large-scale training runs are performed in the cloud.
Which AI PC is right for which use case?
The fastest way to find what you need is to start with your specific use case. Our AI workstations are divided into four profiles for this purpose, each with its own focus on graphics cards, memory, and platform.
 | Local LLM AI Workstation for running local language models
If you want Llama, Mistral, and other open-source models to run directly on your hardware without data leaving your premises, graphics memory is the most important factor. If the model fits entirely into the VRAM, the output speed remains high even for long responses; with up to 96 GB per card, this applies even to the largest open-source models.
For compact assistants with small models, cost-effective NPU systems round out the entry-level options. You can find all these systems in the Local LLM AI Workstations. |
 | Image & Video AI Workstation for Stable Diffusion and Video Generation
In image and video generation, the time per run determines how smoothly you can work. You want to test prompts, models, and parameters in rapid succession without waiting for renderings.
That’s why the Image & Video AI Workstations rely on powerful NVIDIA RTX graphics cards with high computing power. |
 | AI Development Workstation for Prototyping and Fine-Tuning
To develop your own models and perform fine-tuning using methods like LoRA and QLoRA, you need a balance of computing power and memory capacity, but you don’t yet need the resources of a full training system.
The AI Development Workstations bridge this exact gap, ranging from the GeForce RTX 5090 with 32 GB for compact fine-tuning to the RTX PRO 6000 with 96 GB, which can even be used to fine-tune 70B models locally. CUDA, PyTorch, and TensorFlow are all ready to use right out of the box. |
 | AI Training Workstation for Multi-GPU Training
If you’re training your own neural networks from scratch, we offer systems with the highest available performance. The systems scale from a single NVIDIA RTX PRO 6000 with 96 GB, through dual configurations, up to a quad-GPU cluster consisting of four NVIDIA RTX PRO 6000s; in addition, the memory can be expanded up to 768 GB.
You can find all preconfigured systems under AI Training Workstations. |
 | AI PC or AI Server: When Is It Worth Switching to a Rack Solution?
If four graphics cards in a tower case are no longer sufficient, or if the computing power needs to be available to multiple users simultaneously on the network, an AI PC is no longer the right choice. Our AI servers are based on the same principle, featuring up to eight GPUs in a rack-mounted format, redundant power supplies, and remote maintenance via IPMI for continuous operation in the server room rather than at a desk. This is useful, for example, for productive teams that share the same models over the network, or for training tasks that would tie up a single AI PC for days on end.
Suitable systems for these purposes are available among our AI servers. |
Which Workstation for Which Application?
The quickest way to find what you need is to start with your specific use case. Our AI workstations are divided into four profiles for this purpose, each with its own focus on graphics card, memory, and platform.
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Local LLM AI Workstation for running local language models
If you want Llama, Mistral, and other open-source models to run directly on your hardware without data leaving your premises, graphics memory is the most important factor. If the model fits entirely into the VRAM, the output speed remains high even for long responses; with up to 96 GB per card, this applies even to the largest open-source models. For compact assistants with small models, cost-effective NPU systems round out the profile at the lower end.
You can find all these systems in the Local LLM AI Workstations. |
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Image & Video AI Workstation for Stable Diffusion and Video Generation
In image and video generation, the time per run determines how smoothly you can work. You want to test prompts, models, and parameters in rapid succession without waiting for renderings.
That’s why the Image & Video AI Workstations rely on powerful NVIDIA RTX graphics cards with high computing power. |
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AI Development Workstation for Prototyping and Fine-Tuning
To develop your own models and perform fine-tuning using methods like LoRA and QLoRA, you need a balance of computing power and memory capacity, but you don’t yet need the resources of a full training system.
The AI Development Workstations bridge this exact gap, ranging from the GeForce RTX 5090 with 32 GB for compact fine-tuning to the RTX PRO 6000 with 96 GB, which can even be used to fine-tune 70B models locally. CUDA, PyTorch, and TensorFlow are all ready to use right out of the box. |
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AI Training Workstation for Multi-GPU Training
If you’re training your own neural networks from scratch, we offer systems with the highest available performance. The systems scale from a single NVIDIA RTX PRO 6000 with 96 GB, through dual configurations, up to a quad-GPU cluster consisting of four NVIDIA RTX PRO 6000s; in addition, the memory can be expanded up to 768 GB.
You can find all preconfigured systems under AI Training Workstations. |
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AI PC or AI Server: When Is It Worth Switching to a Rack Solution?
If four graphics cards in a tower case are no longer sufficient, or if the computing power needs to be available to multiple users simultaneously on the network, an AI PC is no longer the right choice. Our AI servers are based on the same principle, featuring up to eight GPUs in a rack-mount format, redundant power supplies, and remote maintenance via IPMI for continuous operation in the server room rather than at a desk. This is useful, for example, for productive teams that share the same models over the network, or for training tasks that would tie up a single AI PC for days on end.
Suitable systems for these purposes are available among our AI servers. |
 | How much graphics memory does your AI PC need?
A model that doesn’t fit entirely into the graphics memory can’t be loaded at all, no matter how fast the GPU is. That’s why choosing a GPU starts with VRAM and not with processing power. A simple calculation can serve as a guide. A model with 8 billion parameters occupies about 5 to 6 GB of memory with 4-bit quantization; a model with 70 billion parameters already takes up about 40 GB; and unquantized models require several times that amount. On top of that, there’s memory for the context, which can quickly add up to several gigabytes for long inputs. The following table shows the hardware typically required for the four use cases:
Compact, quantized models with a few billion parameters can even run at the lower end without a dedicated graphics card, using the NPU of energy-efficient compact systems. As soon as larger models, image generation, or fine-tuning come into play, you’ll need a GPU; and for the largest open-source models—run locally and unquantized—you’ll need an NVIDIA RTX PRO 6000 with 96 GB or multiple RTX PRO graphics cards in a cluster.
NVIDIA GeForce or NVIDIA RTX PRO in an AI PC: When Do You Need Which Models?
For image generation, smaller language models, and many development tasks, GeForce cards offer the best balance of computing power and price. The GeForce RTX 5090, with 32 GB of VRAM, already offers substantial reserves. The RTX PRO series takes over as soon as memory requirements exceed that or the system runs at full load continuously. It offers up to 96 GB of VRAM per card with the RTX PRO 6000, cooling solutions designed for continuous operation that can be used side-by-side in a multi-GPU setup, and certified Studio and Enterprise drivers. If speed is the only factor, go with the GeForce. If model size or continuous operation is the deciding factor, the RTX PRO is the way to go.
Multi-GPU in an AI PC: When Multiple Cards Are Worth It
Multiple graphics cards help in two ways. During training, the computational load is distributed across all cards, reducing runtime almost proportionally. During inference of very large models, the graphics memory is combined, allowing a model to fit in memory across multiple cards. Four RTX PRO 6000 cards can provide up to 384 GB of memory. This advantage comes at the cost of higher demands on the overall system, as each additional card requires a full PCIe connection, space in the case, power supply reserves, and a cooling solution capable of dissipating the combined heat output. That’s why multi-GPU setups only make sense on platforms with sufficient PCIe lanes and, for most projects, are limited to two to four cards.
What is an NPU, and what role does it play in an AI PC?An NPU (Neural Processing Unit) is a processing unit built exclusively for neural network computations. It is directly integrated into modern processors such as Intel Core Ultra and AMD Ryzen AI and performs the matrix operations of AI models using a fraction of the power consumption of a graphics card; current generations achieve 40 to over 50 TOPS. An NPU does not have its own graphics memory; instead, it accesses the system’s main memory directly. In NPU-based systems, therefore, the capacity and bandwidth of the RAM determine which models run smoothly.
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 | All Set Up: The AI PC’s Software Environment
An underestimated time sink in AI projects is software setup. Drivers, the CUDA version, and the corresponding builds of PyTorch or TensorFlow must work together seamlessly; otherwise, the project begins with troubleshooting instead of producing results. That’s why every MIFCOM AI PC is delivered, upon request, with a fully configured and optimized AI environment. For GPU systems, this includes drivers, CUDA, and the most common frameworks; for NPU systems, interfaces such as Intel OpenVINO and AMD Ryzen AI, along with tools like Ollama and LM Studio, are preconfigured. You simply upload your project to the system and can start your AI workflow right away. |
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How much graphics memory does your AI PC need?
A model that doesn’t fit entirely into the graphics memory can’t be loaded at all, no matter how fast the GPU is. That’s why choosing a GPU starts with VRAM and not with processing power. A simple calculation can serve as a guide. A model with 8 billion parameters occupies about 5 to 6 GB of memory with 4-bit quantization; a model with 70 billion parameters already takes up about 40 GB; and unquantized models require several times that amount. On top of that, there’s memory for the context, which can quickly add up to several gigabytes for long inputs. The following table shows the hardware typically required for the four use cases:
Compact, quantized models with a few billion parameters can even run at the lower end without a dedicated graphics card, using the NPU of energy-efficient compact systems. As soon as larger models, image generation, or fine-tuning come into play, you’ll need a GPU; and for the largest open-source models—run locally and unquantized—you’ll need an NVIDIA RTX PRO 6000 with 96 GB or multiple RTX PRO graphics cards in a cluster.
NVIDIA GeForce or NVIDIA RTX PRO in an AI PC: When Do You Need Which Models?
For image generation, smaller language models, and many development tasks, GeForce cards offer the best balance of computing power and price. The GeForce RTX 5090, with 32 GB of VRAM, already offers substantial reserves. The RTX PRO series takes over as soon as memory requirements exceed that or the system runs continuously at full load. It offers up to 96 GB of VRAM per card with the RTX PRO 6000, cooling solutions designed for continuous operation that can be used side-by-side in a multi-GPU setup, and certified Studio and Enterprise drivers. If speed is the only factor, go with the GeForce. If model size or continuous operation is the deciding factor, the RTX PRO is the way to go.
Multi-GPU in an AI PC: When Multiple Cards Are Worth It
Multiple graphics cards help in two ways. During training, the computational load is distributed across all cards, reducing runtime almost proportionally. During inference of very large models, the graphics memory is combined, allowing a model to fit in memory across multiple cards. Four RTX PRO 6000 cards can provide up to 384 GB of memory. This advantage comes at the cost of higher demands on the overall system, as each additional card requires a full PCIe connection, space in the case, power supply reserves, and a cooling solution capable of dissipating the combined heat output. That’s why multi-GPU setups only make sense on platforms with sufficient PCIe lanes and, for most projects, are limited to two to four cards. Anything beyond that can be found in our AI servers.
What is an NPU, and what role does it play in an AI PC?
An NPU (Neural Processing Unit) is a processing unit designed exclusively for neural network computations. It is directly integrated into modern processors such as Intel Core Ultra and AMD Ryzen AI and performs the matrix operations of AI models using a fraction of the power consumption of a graphics card; current generations achieve 40 to over 50 TOPS. An NPU does not have its own graphics memory; instead, it accesses the system’s main memory directly. In NPU-based systems, therefore, the capacity and bandwidth of the RAM determine which models run smoothly.
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Set Up and Ready to Go: The AI PC’s Software Environment
An often-underestimated time sink in AI projects is setting up the software. Drivers, the CUDA version, and the corresponding builds of PyTorch or TensorFlow must work together seamlessly; otherwise, the project will start with troubleshooting instead of producing results. That’s why every AI PC from MIFCOM comes with a pre-configured and fully integrated AI environment upon request. For GPU systems, this includes drivers, CUDA, and the most common frameworks; for NPU systems, interfaces such as Intel OpenVINO and AMD Ryzen AI—along with tools like Ollama and LM Studio—are preconfigured. You simply upload your project to the system and can immediately begin your AI workflow.
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AI PC or Cloud: When Local Hardware Pays Off
More and more AI workloads are migrating from the cloud to local systems, and there are three solid reasons for this. The first is data protection. On a local AI PC, customer documents, source code, and research data never leave your own premises, which significantly simplifies compliance issues. The second reason is cost. Cloud GPUs and API calls are billed based on usage; for daily work, owning your own hardware often pays for itself within a year, and after that, every additional GPU hour effectively costs only electricity. The third reason is availability. Your hardware is exclusively at your disposal at all times, without having to wait in line for available instances and without latency to external infrastructure. The cloud remains the go-to option for rare, very large training runs that would overwhelm even a four-GPU system. Many teams therefore take a two-pronged approach, handling development, inference, and day-to-day operations locally on the AI PC, while the few large-scale training runs are performed in the cloud. |
FAQ: | What sets an AI PC apart from a regular PC?An AI PC is designed for local AI applications such as language models or image generation. The differences lie in the hardware specifications: | |
What graphics card do I need for an AI PC?The graphics card is the most important component in an AI PC, but the right choice depends on the workload: | ||
How much VRAM does an AI PC need for local language models?That depends on the model size and quantization. As a rough guide: | ||
For an AI PC, should I prioritize the CPU or the GPU?For AI performance, only the graphics card matters. The processor does not handle any direct AI workloads in an AI PC (with the exception of an NPU). Only when multiple graphics cards are required for AI tasks does a CPU platform with sufficient PCIe lanes become a factor, such as one based on AMD Threadripper PRO or Intel Xeon. | ||
How much RAM does an AI PC need?That depends on the use case: | ||
How much storage does an AI PC need for AI projects?For most AI projects, 1 to 2 TB of fast NVMe storage is sufficient; for large datasets or multiple parallel model versions, more is recommended. The checkpoints from a single fine-tuning session alone can quickly add up to several hundred gigabytes. During training, the SSD even plays a role in determining GPU performance. If the SSD cannot retrieve files quickly enough, the storage will slow down even the most expensive graphics card. | ||
How much power does an AI PC need from its power supply?For a single high-end graphics card such as the RTX 5090 or RTX PRO 6000, a power supply ranging from 850 to 1,000 watts is reliably sufficient. With two graphics cards, the requirement increases to 1,200 to 1,600 watts; with three or four cards, multiple power supplies or a server power supply with over 2,000 watts are standard. | ||
Do I already need an AI PC with multiple GPUs for AI development?Generally not. Prototyping and developing your own models usually run on a single, sufficiently equipped graphics card. Multi-GPU is only worthwhile during the actual training of large models or if a very large model can only be loaded by utilizing the combined memory of multiple cards. | ||
Is a GeForce graphics card sufficient for an AI PC?For inference on smaller models, image generation, and many development tasks, an NVIDIA GeForce RTX model is sufficient, graphics memory is the most important factor here. For production environments with multiple users, continuous operation, or very large models, NVIDIA RTX PRO cards with more VRAM and certified drivers are the more reliable choice. | ||
Which AI frameworks does an AI PC support?Every AI PC from MIFCOM can be equipped with the most common frameworks and tools: PyTorch, TensorFlow, NVIDIA CUDA, Hugging Face Transformers, Ollama, and LM Studio. NPU systems also utilize Intel OpenVINO and AMD Ryzen AI. | ||
Can I add more GPUs to an AI PC later on?That depends on the platform you choose. Systems based on AMD Threadripper PRO or Intel Xeon offer enough PCIe lanes for multiple graphics cards right from the start, so you can add more GPUs later, provided the case and power supply are sufficiently sized. | ||
Why a local AI PC and not cloud services?A local AI PC is especially worthwhile for regular use: Cloud GPUs are billed by the hour, while your own hardware often pays for itself within a year with daily use; after that, each additional hour costs only the price of electricity. In addition, customer documents, source code, and training data remain in-house rather than with an external provider, and the system is exclusively available to you at all times, without having to wait in line for free instances. | ||
Are the AI PCs at MIFCOM configurable?Yes, you can customize every MIFCOM AI PC to suit your specific use case. You can configure the graphics card, processor, RAM, and storage yourself in the configurator, including, if desired, a pre-set AI environment with drivers, CUDA, and the appropriate frameworks. | ||
How do I find the right AI PC?The right AI PC depends on your use case, which determines the required hardware: Local language models, image generation, development, and training each have different VRAM and RAM requirements. Our AI workstations provide an overview of all four profiles. If you’re unsure which profile suits your project, our B2B team will advise you directly on your specific workload and the appropriate configuration. | ||
Configure Your AI PC Now
Now that you’re familiar with the different requirements for an AI PC, the use case, the graphics card, and the platform, you can build your system component by component in the Workstation Configurator, or start by getting an overview of all four profiles in our AI Workstations category. You can find a complete overview of all categories on our Workstations page.
Businesses, research institutions, and public organizations also benefit from dedicated account managers, quotes for bids, purchase on account, or leasing. Contact our B2B team directly, by phone, email, or live chat.
FAQ: Frequently Asked Questions About AI PCs
What sets an AI PC apart from a regular PC?An AI PC is designed for local AI applications such as language models or image generation. The differences lie in the hardware specifications: |
What graphics card do I need for an AI PC?The graphics card is the most important component in an AI PC, but the right choice depends on the workload: |
How much VRAM does an AI PC need for local language models?That depends on the model size and quantization. As a rough guide: |
For an AI PC, should I prioritize the CPU or the GPU?For AI performance, only the graphics card matters. The processor does not handle any direct AI workloads in an AI PC (with the exception of an NPU). Only when multiple graphics cards are required for AI tasks does a CPU platform with sufficient PCIe lanes become a factor, such as those based on AMD Threadripper PRO or Intel Xeon. |
How much RAM does an AI PC need?That depends on the use case: |
How much storage does an AI PC need for AI projects?For most AI projects, 1 to 2 TB of fast NVMe storage is sufficient; for large datasets or multiple parallel model versions, more is recommended. The checkpoints from a single fine-tuning session alone can quickly add up to several hundred gigabytes. During training, the SSD even plays a role in determining GPU performance. If the SSD cannot retrieve files quickly enough, the storage will slow down even the most expensive graphics card. |
How much power does an AI PC need from its power supply?For a single high-end graphics card such as the RTX 5090 or RTX PRO 6000, a power supply ranging from 850 to 1,000 watts is more than sufficient. With two graphics cards, the requirement increases to 1,200 to 1,600 watts; with three or four cards, multiple power supplies or a server power supply with over 2,000 watts are common. |
Do I already need an AI PC with multiple GPUs for AI development?Generally not. Prototyping and developing your own models usually run on a single, sufficiently equipped graphics card. Multi-GPU is only worthwhile during the actual training of large models or if a very large model can only be loaded by utilizing the combined memory of multiple cards. |
Is a GeForce graphics card sufficient for an AI PC?For inference on smaller models, image generation, and many development tasks, an NVIDIA GeForce RTX model is sufficient, graphics memory is the most important factor here. For production environments with multiple users, continuous operation, or very large models, NVIDIA RTX PRO cards with more VRAM and certified drivers are the more reliable choice. |
Which AI frameworks does an AI PC support?Every AI PC from MIFCOM can be equipped with the most common frameworks and tools: PyTorch, TensorFlow, NVIDIA CUDA, Hugging Face Transformers, Ollama, and LM Studio. NPU systems also utilize Intel OpenVINO and AMD Ryzen AI. |
Can I add more GPUs to an AI PC later on?That depends on the platform you choose. Systems based on AMD Threadripper PRO or Intel Xeon offer enough PCIe lanes for multiple graphics cards right from the start, so you can add more GPUs later, provided the case and power supply are sufficiently sized. |
Why a local AI PC and not cloud services?A local AI PC is especially worthwhile for regular use: Cloud GPUs are billed by the hour, while your own hardware often pays for itself within a year with daily use; after that, each additional hour costs only the cost of electricity. In addition, customer documents, source code, and training data remain in-house rather than with an external provider, and the system is exclusively available to you at all times, without having to wait in line for free instances. |
Are the AI PCs at MIFCOM configurable?Yes, you can customize every MIFCOM AI PC to suit your specific use case. You can configure the graphics card, processor, RAM, and storage yourself in the configurator, including, if desired, a pre-set AI environment with drivers, CUDA, and the appropriate frameworks. |
How do I find the right AI PC?The right AI PC depends on your use case, which determines the required hardware: Local language models, image generation, development, and training each have different VRAM and RAM requirements. Our AI workstations provide an overview of all four profiles. If you’re unsure which profile suits your project, our B2B team will advise you directly on your specific workload and the appropriate configuration. |
Configure Your AI PC Now
Now that you’re familiar with the different requirements for an AI PC, the use case, the graphics card, and the platform. Build your system component by component in the Workstation Configurator, or start by getting an overview of all four profiles in our AI Workstations category. You’ll find a complete overview of all categories on our Workstations page.
Businesses, research institutions, and public organizations also benefit from dedicated account managers, offers for competitive bids, purchase on account, or leasing. Contact our B2B team directly, by phone, email, or live chat, to discuss these options.
Our references
Our references