Can the Swiss LLM Compete?

In late summer 2025, Switzerland will release its own Large Language Model (LLM), developed by ETH Zurich, EPFL, and the Swiss National Supercomputing Centre (CSCS). The project emphasizes transparency, data privacy, and linguistic diversity – positioning itself as a European alternative to AI giants like OpenAI, Anthropic, Meta, or xAI.

But how realistic is this goal? Can a publicly funded model truly compete with billion-dollar projects from Silicon Valley? We've examined all available facts – objectively, critically, and without tech hype.

What's Behind the Swiss LLM?

The model is part of the Swiss AI Initiative, launched in late 2023. Key highlights:

• Open Source: Published under Apache-2.0 – including source code, weights, and training data
• Model Sizes: Two variants with 8 billion and 70 billion parameters
• Multilingualism: Trained on more than 15 trillion tokens across 1,500+ languages – 40% non-English
• Infrastructure: Developed on the new Alps supercomputer at CSCS with over 10,000 NVIDIA GH200 Grace-Hopper chips
• Data Privacy: Compliant with GDPR, EU AI Act, and Swiss data protection laws

Research: Well-Staffed but Not Over-Funded

ETH Zurich and EPFL rank among the world's leading universities for engineering and natural sciences. In artificial intelligence, they're well-positioned:

• Prof. Andreas Krause (ETH) is an internationally recognized expert in Reinforcement Learning
• Prof. Martin Jaggi (EPFL) leads the Machine Learning & Optimization Lab
• Since 2024, the Swiss National AI Institute has strengthened collaboration between research and application

However, ETH and EPFL cannot match the salaries and resources of OpenAI or xAI. They offer something different – an environment for open, ethically-oriented research. For a public model like the Swiss LLM, this is a solid foundation.

Computing Power: Strong – But Not Competitive

The Swiss LLM is trained on the new Alps supercomputer, operational at CSCS since September 2024:

• 10,752 NVIDIA GH200 Grace-Hopper chips
• Computing power: 270–435 PFLOPS
• Ranked 6th on the TOP500 list (June 2024)

For comparison:

• GPT-4 was reportedly trained with approximately 25,000 A100 GPUs over 90–100 days
• Grok 4 from xAI uses the Colossus supercomputer with up to 200,000 NVIDIA H100 GPUs

Conclusion: For an academic project, Alps is powerful. But compared to the massive data centers of major tech companies, it falls significantly behind – affecting training speed and model size.

Training Data: Quality Over Quantity

The Swiss LLM was trained on approximately 15 trillion tokens. Particularly noteworthy is the high proportion of non-English data (40%) and coverage of over 1,500 languages – including rare ones like Romansh or Zulu.

The data was ethically sourced – without illegal scraping, respecting robots.txt and copyright requirements. While this limits access to certain specialized information, CSCS emphasizes: «For general tasks, this doesn't lead to measurable performance losses.»

Linguistic Diversity: Where the Swiss LLM Leads

Support for over 1,500 languages is currently unique – even compared to commercial models:

This breadth is particularly relevant for:

• SMEs with international audiences
• Organizations with multilingual communication
• Applications in linguistically diverse countries

Transparency & Data Privacy: Advantage with Compromises

The Swiss LLM is completely open – code, weights, training data: all public. It meets the requirements of GDPR, EU AI Act, and Swiss data protection regulations.

This makes it attractive for:

• Government agencies and institutions
• Companies in regulated industries
• Research and education

However: Avoiding certain data sources – such as medical literature – may limit performance in specialized tasks. Commercial models have advantages here because they can access proprietary content.

Model Comparison: How Does the Swiss LLM Perform?

What Does This Mean in Practice?

The Swiss LLM won't be the most powerful AI on the market. But it's a strong tool for many concrete applications – especially in Europe:

Suitable for:

• Multilingual chatbots and customer support
• Text summarization and translation
• Applications in regulated sectors (e.g., healthcare)
• Research, education, and open-source projects

Not suitable for:

• Highly complex reasoning tasks
• Multimodal applications (e.g., speech + image + video)
• Performance at GPT-4o or Grok level

Conclusion: An Important Model – With Clear Focus

The Swiss LLM is not a miracle model. But it's a responsibly developed, transparent, and linguistically comprehensive AI system that excels precisely where commercial models often have deficits: in data privacy, openness, and regulatory security.

In a market increasingly dominated by «black-box» models, Switzerland is deliberately setting a different tone. Whether this model succeeds will be determined in the coming months – depending on how actively the community develops it and how strategically it's deployed.

The Swiss LLM demonstrates that even without billion-dollar budgets, respectable AI models can be developed that set new standards in crucial areas like data privacy and transparency.

Sources:

• ETH Zurich AI Center
• EPFL Machine Learning Lab
• TOP500 – Alps Supercomputer
• Colossus Supercomputer (xAI)
• Swiss AI Initiative