Polish LLM Deployment on AWS – Bielik & PLLuM with SageMaker & EC2

Polish language applications often struggle with global LLMs. Models like Bielik and PLLuM are designed expressly for Polish, offering superior accuracy and domain relevance.

Why local Polish models?

• Native accuracy – Bielik & PLLuM are trained on large, curated Polish corpora, outperforming general English-centric LLMs on grammar, morphology and slang.
• Regulatory fit – Running inference inside a Polish AWS Region keeps all personal and financial data within national borders, easing GDPR, medical data and KNF cloud audits.
• Operational control – You choose when to upgrade weights; no silent model swaps or provider outages.
• Unlimited capacity – Dedicated GPU or Inferentia2 instances scale horizontally with SageMaker/EC2 Auto Scaling, so burst traffic never hits “capacity reached” screens.

Example scenarios:

• Healthcare – Polish-language discharge summaries, symptom triage chatbots inside hospital VPCs.
• Finance & insurance – Generate or review Polish credit agreements; internal policy Q&A with full audit trail.
• Public administration – Citizen service portals that answer in clear, formal Polish without leaking data to foreign APIs.
• E-commerce & media – Product descriptions, sentiment analysis and SEO in natural, region-specific language.
• Telecommunications – Polish voice-to-text → Bielik chat completion → dynamic response, all on GPU fleet sized for call-centre peaks.

We deploy your selected Polish LLM - whether Bielik (7B/11B) or PLLuM (8B–70B) - on AWS infrastructure:

• Amazon SageMaker Inference (vLLM-powered) or EC2 with GPU
• Auto-scale setup, load balancing and graceful lifecycle management
• Secure VPC deployment, IAM and logging
• Preconfigured endpoints for chatbots, RAG, agents, contact-center workflows
• Optional integration with vanilla APIs or front-end layers
• Training your team on managing endpoints, scaling, security and cost
• Performance validation using your sample Polish scenarios (e.g., policy Q&A, compliance checks)

After the engagement you can promote the stack to production in days, plug it into Bedrock-style agents or layer retrieval-augmented generation on your own vector store—without re-architecting.