Key Benefits
GPU Acceleration
Optional GPU configurations with NVIDIA cards for accelerated training and inference of deep learning models
High-Core CPUs
Multi-core processors perfect for parallel processing, data preprocessing, and CPU-based ML algorithms
Large Storage
Massive storage capacity for training datasets, model checkpoints, and experimental results
Scalable Resources
Easily upgrade RAM, storage, or add GPUs as your models and datasets grow in complexity
Popular Open-Source LLM Models
Our infrastructure is optimized for hosting, training, and fine-tuning open-source large language models that you can fully control and deploy on your own hardware
Gemma (Google)
Deploy Google's open-source Gemma models (2B, 7B, 12B) built with the same research and technology as Gemini. Lightweight, efficient, and commercially permissive with Apache 2.0 license.
Qwen (Alibaba)
Host Alibaba's Qwen (Tongyi Qianwen) models with exceptional multilingual capabilities, especially for Chinese and Asian languages. Strong performance on reasoning, math, and code generation tasks.
LLaMA 2 & LLaMA 3
Deploy Meta's open-source LLaMA models (7B to 70B parameters) for efficient inference and fine-tuning. Excellent performance with lower resource requirements and fully permissive for commercial use.
Mistral & Mixtral
Host Mistral AI's open-weight models like Mistral 7B and Mixtral 8x7B (Mixture of Experts) for advanced reasoning, multilingual capabilities, and efficient inference with Apache 2.0 license.
Falcon
Deploy TII's Falcon models (7B, 40B, 180B) trained on high-quality web data. Strong multilingual performance with permissive licensing for commercial production deployments.
BERT & Transformers
Train and deploy BERT, RoBERTa, DistilBERT, T5, and other Hugging Face transformer models for NLP tasks like classification, NER, question answering, and semantic search.
MPT & BLOOM
MosaicML's MPT and BigScience's BLOOM offer strong multilingual capabilities (176B parameters for BLOOM) with open-source licenses perfect for research and commercial use.
Custom Fine-Tuning
Use LoRA, QLoRA, or full fine-tuning techniques to adapt any open-source LLM to your specific domain with your proprietary data while maintaining complete data sovereignty in Switzerland.
Ideal For
Deep Learning Training
Train neural networks with TensorFlow, PyTorch, or JAX on powerful hardware
Model Inference
Deploy production ML models with low-latency predictions at scale
Data Science
Large-scale data analysis with Python, R, and Jupyter notebooks
Computer Vision
Image and video processing with OpenCV and custom trained models
NLP & LLMs
Natural language processing and large language model fine-tuning
Research Projects
Academic and corporate AI research with dedicated resources