AI Infrastructure your own AI in a Swiss datacenter.
Sensitive data does not belong in a US cloud. We run your own AI in our Swiss datacenters, on Mac Studio and NVIDIA DGX Spark, shared or dedicated. Private AI with local models, full data protection and data location Switzerland, independent of the big US providers.
An API key to a US cloud is not a data-protection concept.
Private AI means: your models, your data, in Switzerland.
Use AI without giving your data out of your hands.
Your own AI
Your own models on your own hardware in our datacenter, instead of shared endpoints at a US provider.
Data protection by design
Sensitive and protected data stays isolated and encrypted, access tightly controlled, even from us.
Data location Switzerland
Processing and storage 100% in Switzerland, in our datacenters ZRH-01 and ZRH-02.
Independent of US clouds
No CLOUD Act, no dependence on a single hyperscaler, Swiss law and Swiss operation.
Local LLMs & RAG
Open models, grounded on your knowledge via RAG, precise answers without data leaving the house.
Operated by us
We set up, keep current and monitor around the clock, with response per agreed SLA.
Request, model and knowledge base, all in Switzerland.
Mac Studio: lots of memory, quiet, efficient.
Apple Silicon with large unified memory is ideal for running local LLMs cost-effectively. Even larger models fit straight into memory thanks to up to several hundred GB of shared memory.
- Lots of unified memory, large models run without expensive specialist GPUs.
- Energy-efficient and quiet, low operating cost per inference.
- Compact and mature, quickly provisioned in the datacenter.
- Ideal for inference of local models and knowledge assistants (RAG).
NVIDIA DGX Spark: an AI supercomputer in a compact form.
The DGX Spark brings dedicated AI performance with the GB10 Grace Blackwell superchip and 128 GB of unified memory, plus the full NVIDIA and CUDA ecosystem, into a compact node, for inference and fine-tuning.
- Full CUDA ecosystem, compatible with common AI frameworks and tools.
- 128 GB unified memory for inference and fine-tuning of local models.
- Compact, dedicated node, can be paired for larger models.
- Ideal when maximum GPU compatibility and fine-tuning are required.
What we typically provide.
| Mac Studio | Apple Silicon, very large unified memory, energy-efficient |
| NVIDIA DGX Spark | GB10 Grace Blackwell, 128 GB, full CUDA ecosystem |
| Model | shared or dedicated |
| Larger GPU clusters | NVIDIA L40S / H100 and more, on request |
| Further hardware | on request |
| Models | Llama · Mistral · Qwen · Gemma · DeepSeek · Phi · your own |
| Serving | Ollama · vLLM · LM Studio · TGI |
| RAG | vector DB · embeddings · retrieval |
| Fine-tuning | LoRA · QLoRA · full fine-tune |
| APIs | OpenAI-compatible endpoints |
| Sites | ZRH-01 Zurich City · ZRH-02 Zurich Rümlang |
| Data location | Switzerland · 100% |
| Monitoring | 24/7, response per SLA |
| Availability | up to 99.9% SLA |
| Isolation | tenant separation, encryption, access control |
Open models we run on your infrastructure.
We are vendor-independent and run the leading open, locally runnable language models, depending on task, language and available memory, among them:
- Llama (Meta)
- Mistral & Mixtral
- Qwen (Alibaba)
- Gemma (Google)
- DeepSeek (open weights)
- Phi (Microsoft)
- as well as Falcon, GPT-OSS and further open models on request
With RAG (retrieval-augmented generation) these models answer based on your own documents, traceable and up to date, without the data leaving your environment.
What companies ask about their own AI infrastructure.
The Mac Studio offers a lot of unified memory on Apple Silicon at moderate cost. This lets larger models fit straight into memory, without expensive specialist GPUs. On top of that comes excellent efficiency: low power draw, quiet operation and low operating cost per request. Ideal for the inference of local LLMs and knowledge assistants.
The DGX Spark brings dedicated AI performance in a compact form with the GB10 Grace Blackwell superchip and 128 GB of unified memory. The big advantage is the full NVIDIA and CUDA ecosystem: nearly every AI framework and tool runs out of the box. It is especially suited to fine-tuning and inference, and several nodes can be paired for larger models.
Roughly: the Mac Studio shines at inference with large memory at low operating cost, the DGX Spark at maximum GPU compatibility and fine-tuning in the CUDA ecosystem. We often recommend one or the other depending on the use case, sometimes both. We advise vendor-independently.
Shared means you share the hardware with other tenants, cleanly isolated, which is the affordable entry point. Dedicated means a device is exclusively yours, with full performance and maximum isolation. We run both in our Swiss datacenters.
Yes. Models, data and the RAG knowledge base reside and run in our datacenters ZRH-01 and ZRH-02. There is no data outflow to US clouds, no CLOUD Act, processing and storage 100% in Switzerland.
Open models like Llama, Mistral, Qwen, Gemma, DeepSeek or Phi run fully on your infrastructure. You keep the weights, the control and data sovereignty, no external provider is involved.
RAG (retrieval-augmented generation) connects a language model with your own knowledge base. Instead of generic answers, the model answers based on your documents, contracts and data, traceable and up to date.
Yes. We often start with a shared node or a single device and expand to dedicated hardware or larger GPU clusters as load and use cases grow, without breaking the architecture.
We do. Provisioning, model updates, scaling, monitoring and cost control are handled by our team, with around-the-clock monitoring and response per SLA. You use the endpoints, we keep the layer underneath running.
Your own AI in a Swiss datacenter?
We go through the use case, the right hardware (Mac Studio or DGX Spark) and data protection with you, with concrete options instead of buzzwords.