Koszyk
Time to turn everything into one decision. The final part walks you through the selection path for a local AI workstation: from a sizing formula, through ready-made hardware scenarios, to the full stack. It also shows why the cloud has its problems, namely unpredictable pricing, vendor dependency and data privacy, and when the numbers favor an on-premise solution.
Every watt that goes into a GPU comes out as heat, and that heat sets the real limits of a project. This part covers the power draw of modern accelerators, power density in the rack, and the thresholds where air cooling gives way to liquid and immersion, plus how to choose a form factor: from a fanless BoxPC at the edge to a server in the rack.
The fastest GPU sits idle if the data does not arrive in time. This part shows how to choose storage and networking for local AI: from NVMe Gen5 drives and GPUDirect Storage, through InfiniBand and Spectrum-X Ethernet, to NVIDIA's full set of communication chips, namely BlueField DPUs, ConnectX SuperNICs and the NVLink Switch.
The GPU does the work, but the processor, system memory and platform decide whether the card gets its data in time. This Intel-first part explains how many cores and memory channels you really need, what the AMX accelerator brings to CPU inference, and how to choose a platform: from Core Ultra through Xeon w to Xeon 6 and NVIDIA Grace.
The GPU is the heart of an AI workstation and usually its most expensive part, and also the choice where it is easiest to overpay or buy a card that cannot hold your model. This part shows how to read an accelerator spec for inference and agents: memory types, architectures, precision formats and a full map of NVIDIA chips, from workstations to the data center.
Dobra odpowiedź na pytanie „jaki sprzęt pod lokalne AI?" nie zaczyna się od nazwy karty graficznej. Zaczyna się od zrozumienia, co obciąża sprzęt podczas pracy modelu. W pierwszej części serii rozkładamy lokalne AI na trzy liczby: pojemność pamięci (VRAM), jej przepustowość i moc obliczeniową, oraz pokazujemy, jak kwantyzacja i KV cache decydują o doborze stacji.
Jak chronić dane przy wdrożeniu AI w produkcji? Architektura air-gapped, edge AI i island mode — praktyczny przewodnik dla zespołów IT Security i CISO. Trzy scenariusze wdrożeniowe.
Explore a practical performance comparison between MSI EdgeXpert and NVIDIA DGX Spark - two AI systems built around the NVIDIA Grace Blackwell architecture. Learn what drives the difference in computing power, how the systems differ in configuration and target applications, and what these differences mean for AI inference, model development, edge deployment, and demanding industrial workloads.
As video analytics continue to evolve, the demand for greater computing power is growing. In many systems, CPUs are no longer sufficient when analysis involves multiple cameras or uses real-time deep learning models. In this article, we explain where CPU limitations arise, why GPU acceleration significantly boosts performance, and how to choose the right computing platform for AI vision systems.
Accelerated computing uses GPUs, AI servers, and specialized hardware architectures to significantly speed up workloads such as artificial intelligence, data analytics, computer vision, scientific simulations, and digital twins. Learn how accelerated platforms work, where they provide the greatest performance advantage, and how to choose the right hardware architecture for your application - from edge AI systems to high-performance GPU servers and scalable data center solutions.
