GNTME / Product catalogue
GNTME / Product catalogue
Artificial intelligence is becoming increasingly accessible to individuals who want more control over their computing, data, and digital tools. A personal AI server can provide the processing power needed to run AI models, assistants, automation tools, image generation, coding applications, document analysis, and other workloads locally.
Depending on the workload, users can choose an AI PC, high-performance workstation, mini PC, GPU server, or dedicated home server. In addition, fast NVMe storage, NAS systems, high-capacity drives, and high-speed networking can create a flexible local computing environment.
Running AI locally can provide greater control over personal data and installed applications. Furthermore, users can select the models and software they want to run instead of relying entirely on online AI platforms. However, hardware requirements vary considerably between smaller AI models and larger language, image, and video models.
When choosing hardware, consider GPU performance, VRAM, CPU capability, system memory, storage capacity, network speed, power consumption, cooling, and upgrade options. The ideal configuration depends on the models you want to run and the tasks you want to perform.
A suitable local system can support a wide range of AI applications, including:
For example, users can create a local assistant that works with selected documents, notes, files, and personal information. With suitable software, the system can search this information and provide responses based on the user’s own knowledge base.
A local setup can include several types of hardware:
You do not necessarily need every component. Instead, select hardware according to the size of your models, expected workload, storage requirements, and budget.
A personal AI server is a computer or server that provides local computing resources for AI applications. It can range from a powerful desktop system to a dedicated rack server with one or more GPUs.
A typical configuration may include:
For larger models, GPU memory becomes especially important. Therefore, users should check the VRAM requirements of their chosen models before purchasing hardware.
Cloud AI provides access to powerful computing without requiring users to maintain their own hardware. However, local computing offers a different set of advantages.
For many users, a combination of local and cloud services can provide the best balance between convenience, performance, privacy, and cost.
The GPU is often one of the most important components in an AI computing system. GPUs can perform many calculations in parallel, making them useful for model inference, image generation, video processing, and other demanding workloads.
When selecting a GPU, consider:
A higher-VRAM GPU can make it easier to run larger models without moving workloads to another system.
An AI PC can provide a convenient starting point for users who want local AI capabilities without building a dedicated server.
A more powerful workstation can handle demanding applications such as:
For heavier workloads, a desktop workstation with a dedicated GPU can provide considerably more computing power than a standard laptop.
AI models and related files can require substantial storage capacity. Models, datasets, documents, images, videos, applications, and backups can quickly increase storage requirements.
A local setup can use:
Fast NVMe storage can improve model loading and application performance, while larger HDDs can provide economical capacity for archives and media.
A local AI system may need to communicate with computers, phones, NAS devices, smart-home equipment, and other systems.
Depending on the environment, users can consider:
A faster wired connection can be especially useful when the AI computer accesses large files stored on a separate NAS.
Local processing can give users more control over personal information. A locally hosted system may work with documents, photographs, notes, research, and other private files.
However, local hardware still needs proper protection. Users should consider:
Keeping important data on local hardware does not automatically make it secure. Good security practices remain essential.
AI can also work alongside smart-home systems. Depending on the software and connected devices, users can build systems that help manage:
A local computing system can act as a central processing point while smaller devices collect information around the home.
Before purchasing equipment, consider the following:
These questions can help prevent overbuying or selecting hardware that cannot handle your intended workload.
| Use Case | Suitable Starting Point |
|---|---|
| Basic AI assistant | Modern AI PC |
| Small local models | Desktop with capable GPU |
| AI image generation | GPU workstation |
| Larger language models | High-VRAM GPU system |
| Dedicated home AI | Server or workstation |
| AI development | High-performance workstation |
| AI with large datasets | GPU system + NVMe/NAS storage |
| Multiple devices | Dedicated server + fast network |
Actual requirements depend on model size, quantisation, software, workload, and desired performance.
A personal AI server is a computer or server that runs AI applications and models locally. It can provide computing resources for assistants, LLMs, image generation, automation, and other applications.
Yes. Many AI models can run on a modern desktop, AI PC, workstation, or dedicated server. The required hardware depends on the size of the model and the type of application.
Not necessarily. A desktop or workstation can handle many AI workloads. A dedicated server becomes more useful when you need additional GPUs, memory, storage, or access from multiple devices.
Not always. Smaller models can run on CPUs, but a suitable GPU can significantly improve performance for many AI workloads.
For many users, 32GB provides a useful starting point. More demanding models and applications can benefit from 64GB, 128GB, or more.
The answer depends on the model. Smaller models can work with less VRAM, while larger models may require considerably more GPU memory.
Yes. Many locally installed models can operate without an internet connection once the required software and model files are installed. However, some applications still require online access.
Yes. Suitable local AI software can create a knowledge base from selected documents and files. This can allow the system to search and analyse your own information.
Yes. Depending on the system, you can upgrade RAM, storage, GPUs, networking, and other components. Therefore, choosing an upgrade-friendly platform can be useful when planning for future workloads.
Yes. A NAS can store models, documents, media, datasets, and backups. A faster network connection can improve access to large files stored on the NAS.
