AI is becoming infrastructure, not just a service
For Greek universities, research centers and companies developing artificial intelligence, the strategic question is no longer simply which large language model performs best today. A more important question is what technological foundation they want to depend on, understand and be able to modify in the years ahead.
Exclusive dependence on proprietary AI services creates a new form of technological lock-in. The model, its development road-map, language priorities, pricing structure and often even access conditions remain under the control of an external provider. A Greek company may build an excellent product on top of such a service while controlling very little of the technological foundation on which its product depends.
EuroLLM offers a different path. It does not imply that Europe should isolate itself from global AI research, nor that every American or Asian model should be replaced by a European one. Instead, it creates an open European foundation on which researchers and companies can experiment, adapt models, build products and retain a larger share of technological knowledge within Europe.
For Greece, this matters both economically and strategically.
Greek should be part of the architecture, not an afterthought
Most large language models are trained on datasets dominated by English and a small number of high-resource languages. This creates an inherent disadvantage for languages with a much smaller digital footprint, including Greek.
EuroLLM was designed around a different principle. It covers all 24 official languages of the European Union, alongside additional international languages. Greek is therefore not an optional localization layer added after training. It is one of the languages around which the model family was designed.
This is particularly relevant for applications involving public administration, education, law, healthcare, cultural heritage, translation and enterprise knowledge management.
EuroLLM-9B illustrates the engineering choices required to make this possible. Its 128,000-token vocabulary was designed for multilingual efficiency, while training used approximately four trillion tokens. The training mixture was deliberately adjusted across different phases to strengthen multilingual performance rather than simply allowing English data to dominate the entire process.
The Greek-language results reported in the technical study are significant. EuroLLM-9B performs strongly relative to other European open models on Greek benchmarks, while EuroLLM-9B-Instruct shows particularly strong translation performance into Greek. It is not superior to every competing model on every task, nor should it be presented as such. What matters is that a technically competitive European model now exists in which Greek was an explicit design requirement.
The opportunity is to contribute, not merely consume
The strongest case for Greek research laboratories is not simply that they can download EuroLLM. It is that they can help improve it.
The ecosystem makes available major components of the development process, including base and instruction-tuned models, EuroFilter for multilingual data filtering and EuroBlocks synthetic instruction data. The wider project now includes additional models and datasets.
This gives research groups many more entry points than a proprietary API provides.
Greek teams could build better evaluation suites for contemporary Greek, identify systematic model failures, construct higher-quality Greek datasets, develop specialized adaptations and contribute the results back to the European research ecosystem.
There is considerable room for work on Greek administrative language, legal terminology, scientific writing, educational material, cultural heritage and regional linguistic varieties. These are areas in which local researchers possess expertise and access to resources that a global AI provider may have little commercial incentive to prioritize.
A coordinated Greek contribution could therefore improve EuroLLM while simultaneously creating reusable national research infrastructure.
There is a business case as well
EuroLLM should not be seen purely as an academic project.
For a Greek AI company, an open European model can become the foundation of commercial services that can be customised, evaluated and deployed under the company’s or customer’s control.
That becomes particularly valuable in sectors involving sensitive information or strict governance requirements, such as banking, healthcare, government, legal services and critical infrastructure.
Instead of acting mainly as an intermediary reselling access to a foreign API, a Greek company can develop expertise in model adaptation, retrieval-augmented generation, evaluation, inference optimisation, domain-specific fine-tuning and secure deployment.
Those capabilities are themselves marketable products.
They also create a more resilient business model. If applications are designed around portable models and open interfaces, companies have greater freedom to replace one model with another as technology advances. This reduces strategic dependency on a single vendor and keeps more engineering expertise and economic value inside the local ecosystem.
Digital independence does not mean technological autarky
Digital independence is sometimes misunderstood as an argument that every country should build every technological component itself. That would make little sense for a country the size of Greece, especially when training frontier-scale models requires enormous amounts of capital, data and computing power.
A more useful definition is the ability to retain meaningful choices.
An organisation should be able to run strategically important models on infrastructure it controls. It should have access to model weights and technical components when required. It should be able to customise the system, audit its behaviour, change suppliers and preserve the internal skills needed to operate the technology.
This is precisely where the European scale becomes valuable.
EuroLLM combines research institutions from several European countries with public European computing infrastructure, including EuroHPC and MareNostrum 5. It therefore demonstrates a model in which individual countries do not need to reproduce the entire AI stack independently. They can instead pool infrastructure while maintaining an open technological commons available to universities, companies and public institutions.
The broader direction of European policy increasingly reinforces this approach. Open-source AI, AI Factories, EuroHPC infrastructure and technological sovereignty are being treated as interconnected elements of Europe’s competitiveness and strategic autonomy.
From users of AI to contributors to AI
For Greece, the strategic choice is therefore not “EuroLLM instead of every other model”.
Greek researchers and companies should continue benchmarking and using the strongest models available for each task. But they also have an interest in ensuring that at least part of their AI capability rests on technology they can study, modify and help govern.
Universities can use EuroLLM in teaching and research. Research centres can develop Greek datasets, benchmarks and specialised models. Startups can build products and sector-specific applications on top of it. Larger companies can invest in deployment, support and integration services.
A particularly valuable initiative would be a coordinated Greek EuroLLM community involving universities, research centers and industry. Its objective could be to maintain high-quality Greek evaluation datasets, contribute legally reusable Greek corpora, develop sector-specific adaptations and test models on real Greek use cases.
Such contributions would benefit EuroLLM, but they would also create domestic expertise that remains valuable even if individual models are eventually replaced.
That is the deeper reason for engaging with the project.
The strategic value of an AI ecosystem cannot be measured only by which model tops a benchmark today. It should also be measured by how much knowledge, infrastructure, data, engineering capacity and freedom of choice a country retains tomorrow.
EuroLLM gives Greece an opportunity to move from being primarily a consumer of foundation models to becoming a contributor to a European AI commons. For Greek research laboratories and AI companies, that is not simply an argument about technological sovereignty. It is an investment in their own long-term technical and commercial capabilities.