A National Language Model: Ambition, Reality and the Price of Sovereignty

Talk of the need for a Russian large language model of its own has gone on for years, but it has so far stayed at the level of declarations and one-off initiatives. A recent Habr post by a practicing specialist invites a sober look at the problem, with neither patriotic pathos nor tech pessimism. The picture that emerges is far more complicated than officials and corporate strategists would like.

The thesis itself is simple: building a competitive LLM from scratch is not a project but an ecosystem. The three pillars of talent, hardware and school sound obvious, but the devil is in the details of each. Start with talent.

This is not just about programmers who know Python and are familiar with the transformer architecture. It takes deep mathematicians able to work at the frontier of optimization theory, distributed computing specialists, and engineers who understand the nuances of training models with hundreds of billions of parameters. There are only a few thousand such people in the world, and most of them are concentrated in the ecosystems of Google, Meta*, OpenAI and a handful of Chinese tech giants.

The Russian market competes for these specialists under clearly unfavorable conditions, and the issue is not only salaries but also access to world-class computing infrastructure.

The hardware situation is even sharper. Training modern LLMs on the scale of GPT-4 or Claude requires clusters of thousands of GPUs in the class of NVIDIA H100 or their equivalents. Sanctions significantly complicate legal deliveries of top-end chips to Russia, and there are as yet no domestic alternatives of comparable performance. Projects such as Elbrus and Baikal address other tasks and trail the leaders in computing power by generations, not years. Parallel imports and workarounds may cover isolated needs, but building systematic training of world-class models on them is a utopia.

However, the author rightly points to the most underrated factor: the existence of a school. The concept is broader than accumulated experience. A school is a culture of engineering decisions passed from project to project and from team to team.

It is institutional memory of thousands of experiments, failed approaches and non-obvious discoveries that cannot be extracted from research papers. OpenAI went from GPT to GPT-4 in five years of continuous iteration. Google DeepMind built up its expertise over more than a decade.

Trying to skip this stage through "effective management" and infusions of budget money is a typical mistake, which the author describes, delicately but precisely, with the remark that "having the desire and the money does not always lead to the desired result."

The context matters: Russia is not starting from zero. Yandex has the YandexGPT family, Sber is developing GigaChat, and other initiatives exist. But the gap between these products and the world leaders remains significant, and it risks growing rather than shrinking, since the pace of frontier model development has only accelerated over the past two years. China, with incomparably greater resources and its own chip production, has still not managed to confidently catch up with the American leaders, although it has come substantially closer thanks to the DeepSeek model and a number of other breakthroughs.

For the industry, this discussion has a very practical dimension. A bet on fully sovereign development means years of investment with no guaranteed result. The alternative is to build expertise in fine-tuning and adapting open models such as Llama or Mistral to the specific tasks of the Russian-language market. That approach is more pragmatic, cheaper and faster to deliver results, although it does not solve the problem of strategic dependence.

Ultimately, the national LLM question is not a technical one but a political and economic one. Is the state ready to invest not in showcase projects but in fundamental infrastructure: education, research centers, access to computing? Is business ready to think in horizons of ten to fifteen years rather than quarterly reports? Until the answers to these questions are clear, talk of a world-class sovereign language model remains more an exercise in strategic thinking than a roadmap.

*Meta is designated an extremist organization and banned in the Russian Federation.