A decade ago, the computing power required to train a 100-billion-parameter language model was out of reach even for the largest research centres. Today? It fits in a single rack. The Blackwell architecture has changed the rules of the game at the hardware level, and the NeMo ecosystem has driven a comparable revolution in software. The result is democratised access to technology that, until recently, was reserved exclusively for Silicon Valley giants.
DGX architecture - from supercomputer to compact workhorse
NVIDIA DGX is not just a “supercomputer in a box”. It is a complete platform designed for the full lifecycle of AI models - from experiments on a single GPU, through distributed training on clusters, to efficient inference in production environments.
Key components of this architecture:
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Tensor Core processors built on the Hopper/Blackwell architecture
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NVLink 4.0/5.0 interconnects with bandwidth of up to 900 GB/s
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InfiniBand NDR 400GB/s networking in a non-blocking fabric configuration
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HBM3e memory with bandwidth of up to 8 TB/s
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The NVIDIA AI Enterprise system stack, optimised for AI
The newest member of the DGX family is DGX Spark - a compact powerhouse designed for data science teams and R&D departments. Standing just 4U tall, this system delivers computing power comparable to entire clusters from a few years ago, at significantly lower energy and space requirements. DGX Spark was designed with inference and fine-tuning scenarios in mind, where memory bandwidth and power efficiency are critical rather than raw processor count. In practice, this means it is possible to run model-inference instances even in the smallest server rooms, without investing in extensive cooling or power infrastructure.
NeMo - a framework for engineers, not only for researchers
The NeMo framework answers the question every CTO and CIO asks themselves: “How do we harness the potential of LLMs without having to build everything from scratch?” NeMo is not just a library - it is a comprehensive ecosystem of engineering tools for designing, training and deploying language models in a secure, controlled manner.
NeMo’s key advantage over other solutions lies in its modular architecture and abstraction layer, which hides the complexity underlying model training and fine-tuning. Thanks to an API oriented around business tasks rather than low-level tensor operations, teams can focus on business value instead of algorithm optimisation.
For example, thanks to NeMo Retriever, implementing a RAG (Retrieval-Augmented Generation) architecture comes down to a few dozen lines of code instead of several thousand - dramatically shortening the time from concept to deployment.
Case studies: not only global players
The application of DGX and NeMo is not limited to technology giants. These solutions are increasingly used by:
KT Corporation - the South Korean telecommunications operator built its own LLM handling the linguistic and cultural context of Korean and English. By applying 3D parallelism techniques and automatic hyperparameter optimisation, the company managed to reduce model training time by 40% compared to standard methods.
UF Health - GatorTron, the largest clinical language model in the US, shows how healthcare-sector data can be used in compliance with HIPAA regulations through generative data synthesis. NeMo and DGX proved key to the process of extracting knowledge from unstructured clinical data.
AI Sweden - Sweden’s AI centre demonstrated that low-resource languages can also have their own large-scale models. Their 100-billion-parameter model covering Nordic languages is an example of effective knowledge transfer between morphologically similar languages.
The Polish context: PLLuM and Bielik on DGX infrastructure
It is worth noting that Polish language models such as PLLuM and Bielik are also trained and run on DGX servers. These ambitious projects, aimed at delivering advanced language models specific to the Polish language, require not only advanced computing infrastructure but also specialist engineering expertise.
DGX infrastructure, with its hardware-software stack optimised for large models, has proven an ideal environment for these projects. Thanks to NeMo, training and fine-tuning processes could be carried out far more efficiently, translating into faster achievement of strong results on demanding language benchmarks.
Guardrails and RAG - security as a priority, not an option
From a systems engineer’s perspective, it is worth highlighting the role of two key components:
Guardrails - these enable precise definition of a model’s operational boundaries. This is not simply about filtering responses, but about advanced mechanisms for verifying and controlling generated content at the semantic level. For example, it is possible to define complex rules governing which types of information may be shared with different categories of users.
RAG (Retrieval-Augmented Generation) - this is more than connecting a knowledge base to an LLM. It is an advanced system for indexing, vectorisation and semantic search that enables the model to “understand” organisational context. Techniques such as hybrid search and re-ranking make it possible to dramatically increase response relevance in specific business domains.
Orchestration and optimisation: MIG and Run:ai
From a systems architecture standpoint, efficient GPU resource management is a critical element. MIG (Multi-Instance GPU) technology enables the logical separation of physical GPUs into smaller instances, which allows for:
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Workload isolation
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Precise resource allocation
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Optimised hardware utilisation
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Flexible scaling
Run:ai, meanwhile, introduces an orchestration layer that treats the GPU cluster as a flexible cloud resource. This makes possible:
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Dynamic resource allocation based on business priorities
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Automatic scheduling of training and inference jobs
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Comprehensive monitoring of resource utilisation
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Management of the AI environment lifecycle
SNOK: from hardware to algorithms
At SNOK, we do not deliver fragments of solutions - we build complete AI implementation paths. Our end-to-end approach covers the entire AI project lifecycle:
Sizing and hardware architecture - we begin with a precise analysis of computing and bandwidth requirements to select the optimal hardware configuration. We take into account not only current needs but also future scaling scenarios, to ensure the best possible TCO.
Model and framework selection - we work with clients to select optimal base models and frameworks for specific business tasks. We analyse trade-offs between accuracy, performance and resource intensity.
Fine-tuning on domain data - we support the process of adapting models to specific business contexts. We use techniques such as PEFT (Parameter-Efficient Fine-Tuning) and LoRA (Low-Rank Adaptation) to achieve maximum efficiency.
Guardrails implementation - we design and deploy security mechanisms that ensure compliance with organisational policies and legal regulations (e.g. GDPR, the AI Act).
Integration with business systems - we build bridges between AI models and existing systems through APIs (such as SAP and UiPath), middleware and dedicated connectors.
Monitoring and optimisation - we deploy systems to monitor performance, accuracy and model drift, ensuring stable operation over the long term.
“What sets companies apart in today’s economy is not merely having AI technology, but the ability to implement it practically and adapt it to real business needs,” says Jacek Bugajski, CEO of SNOK. “Comprehensive platforms that combine hardware, software and expert knowledge are becoming a key tool for building competitive advantage. It is not just about using AI as a fashionable add-on, but about deep integration with business processes and creating solutions that genuinely support a company’s strategy. At SNOK, we deliver exactly this kind of comprehensive platform, tailored to each client’s specific needs.”
Through our partnership with NVIDIA, we deliver solutions that are not only technologically advanced but also secure, cost-effective and compliant with applicable regulations.
The future of AI agents: autonomy and collaboration
Looking ahead, we see a clear trend towards autonomous AI agents capable of:
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Executing complex sequences of tasks
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Making decisions based on long-term objectives
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Adapting to changing business conditions
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Collaborating with each other and with human experts
DGX and NeMo form the technological foundations that make it possible to build such advanced systems today. This is no longer just about models trained on public data - it is about AI agents that understand an organisation’s specifics, operate within its context and support its objectives.
If you are considering building your own AI agent or an entire AI factory - get in touch. At SNOK, we are ready to guide you through the whole process: from concept, through prototyping, to production deployment. Let’s see what we can build together.