AI PCs

An AI PC is a high-performance computer designed for local AI applications. These include local LLMs, AI image generation, coding assistants, upscaling, data analysis and creator workflows.

You can run local chatbots, analyse documents, generate images, write code, edit videos, upscale media or test your own AI projects.

That depends on your use case. Cloud AI often provides access to very large models, while a local AI PC offers greater control, stronger privacy and predictable costs. For regular local workflows, a dedicated AI PC is particularly attractive.

For many AI applications, the graphics card is the most important component. Local LLMs, image generation, rendering and AI acceleration benefit greatly from strong GPU performance and sufficient VRAM.

An NPU can accelerate certain AI functions very efficiently. However, for large local models, AI image generation and many creator or developer workflows, a powerful graphics card remains the most important component.

AI PCs for developers are more heavily focused on local LLMs, coding, APIs, test environments and datasets. AI PCs for creators prioritise image generation, video editing, 3D rendering, upscaling and creative software.

Yes, many AI PCs also make excellent high-performance gaming systems. A powerful graphics card, fast CPU and at least 32 GB of RAM are beneficial for both modern games and local AI workflows.

The amount of VRAM you need depends heavily on your AI workflow. Smaller local LLMs, initial experiments and basic image-generation projects require less graphics memory than large language models, high resolutions or parallel workloads. If you plan to use your AI PC long term, it is advisable to leave plenty of VRAM headroom.

For occasional AI experiments, local chatbots and smaller projects, 32 GB of RAM is often sufficient. For larger LLMs, longer context windows, image generation, video editing, rendering or multiple AI tools running simultaneously, 64 GB of RAM is significantly more comfortable. If you're planning for the future, don't cut memory capacity too close.

For Stable Diffusion, a powerful graphics card with sufficient VRAM is the most important requirement. AI image generation, upscaling and higher resolutions also benefit from ample RAM, a fast NVMe SSD and reliable cooling. If you regularly work with Stable Diffusion, an AI PC with generous VRAM reserves is highly recommended.

ComfyUI benefits particularly from a powerful GPU, plenty of VRAM and fast SSD capacity for models, checkpoints and temporary files. Simple workflows run on modest hardware, but complex node structures, high resolutions and multiple models simultaneously require noticeably more VRAM and system memory.

For Ollama, LM Studio and similar local LLM tools, you should prioritise a powerful GPU, sufficient VRAM, plenty of RAM and a fast NVMe SSD. The larger the model, the longer the context window and the more tasks you run in parallel, the more important graphics memory and system memory become.

Many local LLMs can be run on a suitable AI PC. However, requirements increase significantly with model size, quantisation and context length. Smaller models are much easier to run locally, while larger models require more VRAM, RAM and compute performance. For demanding LLM workflows, it is advisable to choose a system with generous hardware reserves.

AI models, checkpoints, datasets, project files and media assets can quickly consume large amounts of storage. A fast NVMe SSD ensures that models, tools and projects load quickly. For growing AI workflows, sufficient SSD capacity is highly recommended, especially if you work with image generation, video editing or multiple local models.

Many powerful gaming PCs are also suitable for local AI applications, provided that the GPU, VRAM, RAM and SSD meet the workload requirements. For AI, however, it is not just gaming performance that matters, but also graphics memory capacity, stability under sustained workloads and sufficient headroom for models, datasets and parallel tools.

An AI PC is ideal for local LLMs, image generation, coding assistants, creator tools and productive AI workflows on the desktop. An AI workstation is designed for much larger models, extensive datasets, professional applications, larger memory capacities, more powerful GPUs and highly expandable platforms.

An AI PC is particularly worthwhile if you regularly work with local AI tools, do not want to send confidential data to external services or want to avoid API fees for locally executed models. Cloud AI remains practical for very large models and occasional use, while a dedicated AI PC gives you significantly more control over your workflows.

For many AI workflows, the graphics card is the most important component. Local LLMs, Stable Diffusion, ComfyUI, rendering and GPU-accelerated tools benefit from high compute performance and sufficient VRAM. The ideal GPU depends on whether you want to test smaller models, generate images or run large-scale workloads locally.