Use Case

AI & Machine Learning

Powerful dedicated servers optimized for AI training, model inference, and data science workloads

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.

Best For:

Edge deployment, mobile applications, chatbots with limited resources, on-device AI, and cost-efficient inference. Excellent for projects requiring strong performance with minimal hardware.

VRAM Requirements:

2B: 4-8GB | 7B: 16-24GB

12B: 32-48GB

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.

Best For:

Chinese language processing, multilingual Asian applications, international e-commerce, mathematical reasoning, code generation, and applications requiring strong Chinese-English bilingual capabilities.

VRAM Requirements:

Qwen 2.5-7B: 17GB (FP16) or 12GB (quantized)

Qwen 14B: 32-40GB | Qwen 72B: 140-160GB

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.

Best For:

General-purpose chatbots, content generation, code assistance, instruction following, and multilingual tasks. Excellent balance of performance and efficiency.

VRAM Requirements:

7B: 16-24GB | 13B: 32GB

70B: 80GB (quantized) or 140-160GB (full)

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.

Best For:

Complex reasoning tasks, mathematical problem-solving, code generation with advanced logic, multilingual understanding, and cost-efficient inference at scale.

VRAM Requirements:

Mistral 7B: 14-24GB

Mixtral 8x7B: 80GB (quantized) or 110GB (full)

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.

Best For:

High-quality text generation, research applications, multilingual content creation, and large-scale commercial deployments requiring strong data quality.

VRAM Requirements:

7B: 16GB | 40B: 80GB

180B: 320GB+ (Multi-GPU required)

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.

Best For:

Named entity recognition (NER), sentiment analysis, text classification, semantic search, question answering systems, and domain-specific NLP tasks requiring fine-tuning.

VRAM Requirements:

BERT Base: 4-8GB | BERT Large: 12-16GB

T5 Base: 8GB | T5 Large: 16-24GB

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.

Best For:

Multilingual applications (BLOOM supports 46+ languages), cross-lingual transfer learning, international content generation, and research requiring diverse language support.

VRAM Requirements:

MPT 7B: 16GB | MPT 30B: 64GB

BLOOM 176B: 320GB+ (Multi-GPU required)

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.

Best For:

Domain-specific applications (legal, medical, finance), adapting models to company-specific terminology, improving performance on niche tasks, and maintaining data privacy.

VRAM Requirements:

LoRA/QLoRA: 50% less VRAM than full model

Full Fine-Tuning: Same as base model requirements

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

Ready for AI Workloads?

Deploy your dedicated AI server in Switzerland with optional GPU configurations

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.

Best For:

Edge deployment, mobile applications, chatbots with limited resources, on-device AI, and cost-efficient inference. Excellent for projects requiring strong performance with minimal hardware.

VRAM Requirements:

2B: 4-8GB | 7B: 16-24GB

12B: 32-48GB

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.

Best For:

VRAM Requirements:

Qwen 2.5-7B: 17GB (FP16) or 12GB (quantized)

Qwen 14B: 32-40GB | Qwen 72B: 140-160GB

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.

Best For:

General-purpose chatbots, content generation, code assistance, instruction following, and multilingual tasks. Excellent balance of performance and efficiency.

VRAM Requirements:

7B: 16-24GB | 13B: 32GB

70B: 80GB (quantized) or 140-160GB (full)

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.

Best For:

Complex reasoning tasks, mathematical problem-solving, code generation with advanced logic, multilingual understanding, and cost-efficient inference at scale.

VRAM Requirements:

Mistral 7B: 14-24GB

Mixtral 8x7B: 80GB (quantized) or 110GB (full)

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.

Best For:

High-quality text generation, research applications, multilingual content creation, and large-scale commercial deployments requiring strong data quality.

VRAM Requirements:

7B: 16GB | 40B: 80GB

180B: 320GB+ (Multi-GPU required)

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.

Best For:

Named entity recognition (NER), sentiment analysis, text classification, semantic search, question answering systems, and domain-specific NLP tasks requiring fine-tuning.

VRAM Requirements:

BERT Base: 4-8GB | BERT Large: 12-16GB

T5 Base: 8GB | T5 Large: 16-24GB

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.

Best For:

Multilingual applications (BLOOM supports 46+ languages), cross-lingual transfer learning, international content generation, and research requiring diverse language support.

VRAM Requirements:

MPT 7B: 16GB | MPT 30B: 64GB

BLOOM 176B: 320GB+ (Multi-GPU required)

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.

Best For:

Domain-specific applications (legal, medical, finance), adapting models to company-specific terminology, improving performance on niche tasks, and maintaining data privacy.

VRAM Requirements:

LoRA/QLoRA: 50% less VRAM than full model

Full Fine-Tuning: Same as base model requirements

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