Implementing PLLuM in Your Company: How to Choose an Implementation Partner and Where to Start

What PLLuM is in 2026

PLLuM is an open family of models trained for Polish, funded by the Ministry of Digital Affairs. The project started in 2024 within a consortium of universities and institutes, and since 2025 it has been developed by the HIVE AI consortium led by NASK. The models already power solutions such as mObywatel and city offices. No one knows their full adoption scale, since the open license doesn’t require reporting deployments (Rzeczpospolita).

On May 21, 2026, 11 new models joined the family in sizes of 4, 8, 12, and 70 billion parameters (PLLuM project blog). They were released with documentation tailored to the EU AI Act: data sources, processing descriptions, and training and evaluation methods. For companies that must demonstrate AI Act compliance, this is a tangible advantage over many foreign models.

For business, licensing is key—because not every PLLuM variant is suitable for commercial use:

Sources: pllum.org.pl, Hugging Face – CYFRAGOVPL profile, instytut.ai. Always check the license on the specific model card before deployment; it depends on the version, not just the size.

For applications, choose variants labeled “chat” or “instruct,” and leave “base” versions for further training. The Llama 3.1 license used for the 8B and 70B models requires, among other things, including the “Built with Llama” designation in the product or its documentation.

When PLLuM makes sense for your company

PLLuM works best where two conditions meet: data cannot leave the company, and the work is in Polish. Typical use cases include:

• Knowledge assistant on company documents. Employees ask about procedures, contracts, or technical docs; the system answers based on internal files and cites sources.
• Handling inquiries and complaints in Polish. The model classifies messages, extracts key data, and drafts responses for approval.
• Document processing. Summarization, version comparison, and anonymization—modules NASK prepared in its legal assistant for public administration.
• Chat inside your ERP. Comarch used the largest PLLuM variant (70B) to power chat in its ERP system (PFR, interview with AI Lab OPI).

PLLuM isn’t the best choice for multi-step agents, coding, or multilingual tasks; top commercial models have the edge there. In practice, a hybrid architecture often works best:

A sensible split looks like this: PLLuM processes sensitive data on-prem, while a commercial model handles tasks without personal data or trade secrets. When a stronger model is needed, PLLuM can first anonymize a document and only then pass it on. Don’t pick between PLLuM, Bielik, and a foreign model by brand—let tests on your company’s data decide.

Deploying PLLuM from idea to production in five steps

PLLuM’s creators advise organizations to define needs, prepare data and infrastructure, and only then bring in a vendor (pllum.org.pl). That’s sound advice, but most companies don’t have a team to do it alone. Here’s how the path looks with an implementation partner:

1. Scoping workshop. We pick one process and one measurable goal, e.g., customer response time. We define who will use the system, their permissions, and data sources.
2. Data audit and preparation. Documents must be converted to clean text—no OCR errors or stray markup—then split into chunks for retrieval. In parallel, we mirror access controls so the assistant can’t show files a user isn’t allowed to see.
3. Validation on historical data. We build a test set from real questions and ground-truth answers prepared by your experts. We compare PLLuM variants, Bielik, and a commercial model for retrieved passage relevance, answer faithfulness to sources, latency, and cost.
4. User pilot. The system runs for a few weeks with a small group of employees. We collect ratings and improve retrieval, instructions, and safeguards.
5. Integration and operations. We integrate with ERP, CRM, or your document repository, add SSO and monitoring. Quality tests run automatically, so with every new PLLuM release you’ll know if swapping models pays off.

Step 3 usually decides success. Without a test set, you can’t reliably answer whether PLLuM is enough or a larger model is needed.

In RAG systems, retrieval quality often matters more than the generator. Polish inflection makes keyword search harder—“umowa,” “umowy,” and “umowie” are different strings for a search engine. That’s why we combine semantic search based on embeddings tuned for Polish with full‑text search over base word forms (lemmas).

Want to go through these steps with a partner? We run the first one—the scoping workshop—together with your team and deliver a clear recommendation. Book a scoping workshop with SH

Infrastructure, security, and costs

PLLuM’s license is free, but the model needs a server with a GPU. Requirements grow with model size, and beyond the generator you’ll need retrieval and RAG evaluation modules:

Source: technical requirements on pllum.org.pl.

For many enterprise RAG scenarios, 8B or 12B models are enough, and the generator fits on one or two server GPUs. Michał Brzezicki, CTO of SentiOne, notes that Polish models offer strong quality for the cost and run on infrastructure most Polish companies already have (PFR). A 70B model means investing in several H100 or A100‑class cards—choose it only after testing.

The table is a starting point, not a final design. The number of GPUs also depends on traffic—concurrent requests and context length, which occupies the model’s KV cache. Quantization to 8 or 4 bits clearly reduces memory needs, but you must measure its impact on quality on your test set. Serve the model via an inference engine like vLLM to handle many requests in parallel.

Total implementation cost has four parts: buying or renting GPUs, data preparation, integration with company systems, and ongoing operations and monitoring. Owning servers pays off at steady, high load. At low traffic, renting GPUs by the hour or using a pay‑per‑request cloud model can be cheaper.

Running a model on your own servers doesn’t automatically make it safe. NASK described a case where a model recognized a harmful English prompt and refused to answer, but produced harmful content in Polish (PFR, interview with NASK). That’s why we run safety tests in Polish, enforce document access controls, harden against prompt injection, and log every response.

How to choose an implementation partner for PLLuM

An implementation partner is responsible for everything the model doesn’t provide: data, retrieval, integrations, quality tests, and security. Before you choose, ask six questions:

Funding the implementation

Deploying a Polish AI model can be part of a digitization project funded by the EU or a preferential loan. Your application must include the technical scope, budget, and measurable outcomes. As an implementation partner, we help prepare this section so the project can be delivered as planned.

Webinar “PLLuM. Polski model AI. Od rozwoju modelu do pierwszych wdrożeń”

The Polish Development Fund invites you to the webinar “PLLuM. Polski model AI. Od rozwoju modelu do pierwszych wdrożeń,” which will take place on November 24, 2026, from 10:00 to 11:00 (registration at pfr.pl). Project experts will explain how the Polish models are built, what they can do, how they differ from other solutions, and where they are headed.

If you’re planning an implementation, prepare questions about licenses, hardware requirements, and the roadmap. After the webinar, book a scoping workshop with SH: we’ll check whether PLLuM can handle your company’s data.

FAQ about deploying PLLuM

Can PLLuM be used commercially?

Yes, variants released under Apache 2.0 and Llama 3.1. Versions marked “nc” are for research and non‑commercial use only.

Does PLLuM work without the cloud?

Yes. You can run the model locally—on a GPU server in your company or at a chosen data center. Your data doesn’t go to model providers.

How much does it cost to implement PLLuM?

The license is free. Costs include hardware or GPU rental, data preparation, integrations, and operations. A reliable estimate is possible after a scoping workshop, once the process and data are defined.

PLLuM or Bielik?

Both are Polish and open. Let tests on your data—not brand—decide.

Which company will implement PLLuM?

An implementation partner with experience in RAG systems, integrations, and language model quality testing. SH works with PLLuM, Bielik, and commercial models, and selects the model based on tests on the client’s data.

SH – implementation partner for PLLuM and Polish AI models

SH is a Polish AI implementation company operating since 2016. In SH AI Studio we deliver language‑model solutions—from knowledge assistants to multi‑agent systems running in production. We hold ISO 27001 certification, and client data is never used to train models.

We work with manufacturing, finance, and cybersecurity companies, among others. Example: after adding an AI layer to a cybersecurity platform, onboarding time for new users dropped by 95%.

We don’t tie ourselves to a single model. We use PLLuM and Bielik where data sovereignty matters, and commercial models where their strength is needed. The choice is driven by tests on the client’s data.

Start with a scoping workshop. Together we’ll pick one process, review the data, and compare PLLuM with alternatives on your company’s real examples. After the workshop you’ll receive:

• a model recommendation (PLLuM, Bielik, or a commercial model) with justification,
• a preliminary infrastructure and implementation cost estimate,
• a pilot plan with one measurable goal.

Book a scoping workshop with SH