AI Factory
Purpose built datacenters for AI training
NVIDIA and the world’s top computer manufacturers unveiled an array of NVIDIA Blackwell architecture-powered systems featuring Grace CPUs, NVIDIA networking and infrastructure for enterprises to build AI factories and data centers to drive the next wave of generative AI breakthroughs. With datacenter being the new unit of measurement for generative AI training, AI factory systems are the perfect solution.
NVIDIA AI Factory portfolio
Training and delivering AI models at-scale requires equaly capable infrastructure which is also scalable to customers needs. NVIDIA has a complete product portfolio which can build an AI factory under single ecosystem, keeping the controls and management simple, intuitive and accesible from anywhere.
NVIDIA GB200 NVL72
NVIDIA DGX B200
NVIDIA Quantum
Jensen Huang
NVIDIA CEOKey benefits of NVIDIA powered AI system
Simplified solution
Simplify and scale AI deployments and operations with workflow automation.
Tailored to fit
Don't build a system that everyone tries to sell you - build one that you need.
Secure environment
Deploy your solutions into secure on-premises environment without endangering your data.
Accelerating AI with NVIDIA Blackwell
With the AI model size exponentionally growing, the need for adequate hardware in this sector is larger than ever and solid and scalable infrastructure, high power efficiency and data conidentiality are a fundamental qualities to keep the project on track.
As AI became a consumer-level commodity in today’s world, companies have to face various obstacles to satisfy this new, growing demand.
NVIDIA Blackwell architecture brings not only revolutionary performance and efficiency increase, but also a whole new concept of Datacenter purpose – an AI factory. A purpose-built datacenter scale compute clusters optimised for model training, inference, data processing and any other necessities acompanied with AI application development.
Grace Blackwell superchip
Transistor count
FP8 performance
GPU memory
AI Factory networking
NVIDIA RA (reference architecture) recommends a leaf-spine architecture with 400G InfiniBand for high-speed, low-latency connections between GPUs and storage, ensuring optimal performance for AI workloads. The architecture offers dedicated Ethernet switches for in-band and out-of-band management, maintaining a non-blocking network structure. Supermicro’s adaptation of this system scales from 32-node clusters to thousands of nodes, allowing telecom companies to build large-scale AI infrastructures efficiently. The robust network fabric supports NVIDIA GPUDirect RDMA, enhancing data transfer speeds and overall system efficiency, making it ideal for training and deploying large language models (LLMs).
AI Factory compute
AI Factory storage
AI Factories require to process large ammount of training data, which has to be accessed with the lowest latency possible. NVIDIA RA offers various solutions from different vendors, each of them offering innovative solution to this problem. Such system includes GPU-direct working storage for immediate processing needs and tiered storage solutions for data ingestion, preprocessing, and archival. This architecture maximizes performance by ensuring efficient data flow between storage and compute resources. The system supports NVIDIA GPUDirect RDMA, which allows direct data transfers to GPU memory, enhancing speed and reducing latency. This comprehensive storage solution is essential for training and deploying generative AI models, enabling telecom companies to manage vast amounts of data effectively.
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