AI is often associated with cloud services. But for companies dealing with sensitive documents, customer data or business-critical calculations, it's a double-edged sword. When everything is sent away for analysis, both security and privacy risks arise. That's why there is growing interest in local AI models — language models and analytics tools that run entirely within your own network.
With the right hardware, you can get the same capabilities as in the cloud: document analysis, automated structuring, transcribing meetings, and processing large internal datasets. The difference is that everything happens locally, with full control over the data flow.
Swedish AI Models and International Alternatives
Three models that are currently of particular interest for local operation:
The AI Sweden models — available via Hugging Face and with more information at ai.se. These are trained with a focus on the Swedish language and are suitable for everything from customer service solutions to internal text analysis.
Deepseek R1 — developed to work efficiently with larger amounts of text. The model is strong on deductive analysis, research and report generation.
Llama 4 — Meta's latest generation. Available in several sizes, from variants of a few billion parameters to full-scale models of over 100B. Flexible for both developers and businesses that want control over their infrastructure.
What does on-premises use mean in practice when security and sensitive data cannot be in the cloud?
Managing large LLMs is complex, and local solutions are often necessary due to regulatory frameworks such as GDPR, privacy laws in healthcare and research, and procurement requirements. This drives many organizations to build their own AI environments instead of using cloud services.
Examples of application areas for AI solutions in companies:
Document review: Indexing and analyzing thousands of PDFs requires both high RAM space and fast disk I/O. NVME storage with at least 3,500 MB/s read speed is recommended.
Transcription: Speech-to-text models can run on the CPU, but for real-time performance, GPU acceleration is needed. Here the CUDA support from Nvidia is crucial.
Data processing: When hundreds of thousands of data points are to be analyzed in parallel, memory bandwidth becomes a bottleneck. HBM memory or high bandwidth GDDR6X make a difference.
Recommended system requirements for the AI models.
Talking about “minimum requirements” is misleading. Rather, it is a matter of matching between model size and hardware capacity.
CPU: 16—32 cores (AMD Ryzen Threadripper or Intel Xeon) provide sufficient parallelism to handle preprocessing and coordination of GPU loads.
GPU:
For smaller models (<7B parameters): RTX 4070 Ti with 12GB of VRAM may suffice.
For mid-size models (13B—30B): RTX 4090 or equivalent with 24GB VRAM is minimum limit.
For really big models (65B+): Professional cards such as the Nvidia A6000 or H100 are recommended.
RAM: 64 GB should be considered baseline, 128 GB provides space for heavy datasets.
Storage: NVMe SSD, preferably PCIe Gen4 or Gen5, to avoid I/O bottlenecks. At least 2 TB if you work with larger corpora.
For portable systems, compromises are inevitable, but today's AMD processors combined with RTX 4080/4090 mobile variants can do surprisingly well. However, the amount of VRAM becomes the critical constraint.
The machines behind the performance are not always obvious
We at Compliq build workstations and laptops specifically for these requirements. Our configurations are not just based on raw performance, but optimized for AI workloads: accurate cooling, stable power supply, and balanced component selections so that the GPU does not become oversized relative to the CPU or RAM.
This allows you to start a local instance of a Swedish language model without compromising operational security. And because we work with both NVIDIA's latest GPUs and AMD's most powerful processors, we can size the machine exactly according to your needs — from a portable AI laptop to a full-scale training server.
Local AI models are more than an experiment — they are a tangible solution for companies looking to combine AI-driven productivity gains with full control over their data. But succeeding is about understanding the technical requirements and building an infrastructure that meets them.
Therefore, the hardware is as central as the model you choose. Compliq gives you both: computers built to handle heavy AI loads, and guidance on which models are best suited for your application.
For more information on unique AI computer builds please contact:
Anders Malm
