March 2026, San Francisco. Jensen Huang is on stage at NVIDIA GTC — the world’s largest AI conference. Behind him, a map: “The World Building Regional AI With NVIDIA Nemotron”. On that map, next to the US, Japan, and India — Poland. And next to it, one word: Bielik.
That is not a courtesy nod. Bielik is one of a handful of regional models NVIDIA works with directly. A Polish non-profit project — built by volunteers, researchers from Jagiellonian University, and ACK Cyfronet AGH — is now part of the global strategy of the largest technology company on the planet.
If you run a company in Poland and still think “AI in Polish” is science fiction — this article is for you.
What Bielik is and why it should matter to you
Bielik is a family of Polish language models developed by the SpeakLeash Foundation. Not a Silicon Valley corporation — a non-profit with Polish scientists, engineers, and more than 5,200 volunteers.
A few facts:
- Apache 2.0 license — commercial use allowed, no fees, no vendor lock-in
- More than one million downloads on Hugging Face
- 6th place globally among base models on the EuroEval Multilingual Leaderboard
- The first independent model in that ranking — the rest are big-tech products
- 100,000+ users and two million prompts sent on chat.bielik.ai
Why does that matter for your business? Global models — GPT, Claude, Gemini — treat Polish as “one of many” languages. Bielik treats it as a priority.
Regional models — why Europe is building its own AI
At the same conference, NVIDIA announced partnerships with Perplexity and more than a dozen European AI companies. Reuters quotes Kari Briski, NVIDIA VP: “Europe needs strong models that reflect each country’s unique language and culture.”
That is not just PR. NVIDIA helps local teams generate synthetic data in native languages and train reasoning models. Perplexity will distribute them — companies will be able to run them in local data centres.
For Polish businesses it means:
- Data sovereignty — your data does not have to leave for US servers
- Lower cost — a local model on your server means zero per-token API fees
- Better context — models trained on Polish data understand Polish business reality
Tokenizer adaptation — why it changes everything
A tokenizer is the model’s “alphabet”. Before the model processes your sentence, it must split it into pieces — tokens. And here lies the problem.
Standard tokenizers (like GPT’s) are optimised for English. The word “business” might be one token. But a long Polish compound? It can be three or four tokens. A heavily inflected verb? Sometimes five.
In practice that means:
- More expensive — APIs bill per token, so Polish sentences cost 2–3× more than English ones
- Slower — more tokens means longer processing
- Worse quality — the model “sees” Polish words as glued fragments, not wholes
Bielik uses a tokenizer optimised for Polish. It recognises Polish words, endings, and inflection as units. It is the difference between talking through a translator and talking to a native speaker.
Practical example: the sentence “Przygotowaliśmy ofertę dla państwa firmy” — in GPT roughly 8–10 tokens, in Bielik roughly 5–6. Fewer tokens means faster answers and lower cost.
Bielik-Minitron-7B — smaller, faster, still strong
At NVIDIA GTC the Bielik team presented Bielik-Minitron-7B — a compressed version of Bielik-11B, built with NVIDIA engineers.
In short:
- They took an 11-billion-parameter model (50 layers) and trimmed it to 7.35 billion (40 layers)
- The larger model “taught” the smaller one — knowledge distillation
- They tested ten configurations before picking the best trade-off
The numbers:
| Metric | Bielik-11B | Bielik-Minitron-7B |
|--------|------------|-------------------|
| Parameters | 11.04B | 7.35B |
| Tokens/s | 54.42 | 81.41 |
| Recovered quality | 100% (baseline) | 90.1% |
| Size reduction | — | 33.4% |
About 33% smaller, ~50% faster, keeps ~90% of quality. You can run it on a laptop with a decent GPU.
Sebastian Kondracki, CEO of Bielik.AI: “Working with NVIDIA engineers shows that high-performance AI can also be efficient and accessible. Minitron let us build a model that is very accurate yet lightweight.”
How to try Bielik yourself
You do not need to be a developer or own a server room. Three paths — from simplest to advanced.
Option 1: Online chat (zero setup)
Open chat.bielik.ai and start typing. Free, no signup, no install. See how Bielik handles your real business questions.
Option 2: Locally with LM Studio or Ollama (~15 minutes)
- Install LM Studio (lmstudio.ai) or Ollama (ollama.com)
- Search for “Bielik” or “speakleash” in the model catalogue
- Download a GGUF build (e.g. Q4_K_M for quality/speed balance)
- Run and chat — data never leaves your machine
Option 3: Hugging Face for developers
Models are on huggingface.co/speakleash — full Python/Transformers integration. Apache 2.0 — no commercial restrictions.
What you can implement today
- Try chat.bielik.ai on real questions from your industry — support, email drafts, document triage. Five minutes.
- Compare with GPT — ask the same question in both. See which handles Polish business context better (try contracts, terms of service, legal-ish text).
- Install locally if you handle sensitive data — on-device Bielik means no prompt leaves your network.
- Join the community on Bielik’s Discord (5,200+ members) for early model and tooling updates.
- Consider Bielik in your product — if you need solid Polish understanding, Apache 2.0 Bielik is a ready foundation without licence fees.
What you can gain
Let’s run the numbers.
GPT-4o API cost for a company with ~100 Polish queries per day:
- ~500–800 tokens per query (Polish is “expensive” in GPT’s tokenizer)
- ~$0.005 per query → ~$15/month API alone
- Plus: data goes to OpenAI’s servers
Same scenario with local Bielik:
- One-time: a GPU card (~PLN 2–3k) or an existing laptop GPU
- Per-query cost: $0 (local inference)
- Data: never leaves your perimeter
The real value is not only savings. It is sovereignty, Polish-first quality, and independence from Big Tech pricing — the model is yours to run.
Bielik shows that Poland does not have to be only a consumer of global AI. It can co-build it. A volunteer-driven project highlighted on NVIDIA’s stage next to the world’s largest players is worth attention — not because it sounds proud, but because it works.