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Maintenance of GPU systems
Harness the full power of artificial intelligence with an infrastructure that has been specially developed for AI workloads. We support you from architecture consulting and hardware maintenance through to monitoring and reporting – regardless of the manufacturer, but always with a clear focus on your requirements.
What companies need to look out for in AI infrastructures
With the right maintenance concepts, clear architectural advice and continuous monitoring, many of the risks of an AI data center can be minimized – and the opportunities fully exploited. The key is to find the balance between technological potential and operational reality.
Data sovereignty & security
Sensitive company or customer data remains entirely in your own data center – a clear advantage for industries with high compliance or data protection requirements.
Performance & efficiency
Dedicated GPU clusters can be optimally adapted to the specific workloads. Training jobs run without cloud latency, and resources are used in the best possible way thanks to customized architecture.
Profitability & new business models
An on-premise data center is often cheaper than using the cloud when capacity is high. At the same time, the infrastructure opens up the possibility of offering AI services internally or externally as added value.
High initial investment
GPUs, networks, cooling and facility adjustments are capital-intensive. The ROI depends heavily on the actual capacity utilization and the projects.
Complex operation & shortage of skilled workers
AI data centers require special expertise in HPC, DevOps and AI engineering. A lack of personnel can make stable operation considerably more difficult.
Rapid technological change
Hardware evolves in cycles of two to three years. Without a well thought-out maintenance and upgrade strategy, there is a risk that investments will lose value too soon.
Hardware for AI workloads
GPUs are at the heart of an AI data center. They deliver the massively parallel computing power that is essential for neural networks. From the first NVIDIA workstation GPUs such as the Quadro P1000 to the A100 and H100 as the industry standard and the new Blackwell generation with Rubin architecture – the development shows a continuous leap in performance. Rubin uses HBM4 memory and is optimized for both training and inference with long contexts. Disaggregated architectures such as Rubin CPX separate compute and bandwidth requirements – an approach that further increases efficiency. AMD with the Instinct MI300 series and Intel with the Arc Pro B series are also bringing powerful alternatives to the market.
But even the most powerful GPU can only unfold its potential in a suitable system environment. NVIDIA offers a complete portfolio here:
- DGX platform: The AI factory for developing and providing models.
- HGX platform: Basis for AI and HPC supercomputers.
- IGX platform: With a focus on functional security and edge scenarios.
- MGX platform: Modular architecture for flexible, accelerated computing.
- OVX systems: Scalable infrastructure for high-performance AI and digital twins.
- Grace CPU: Architecture that brings data processing and AI workloads closer together.
In addition, partners such as Supermicro or Dell provide GPU-optimized servers that serve as the basis for individual architectures – with up to eight GPUs per node and NVSwitch technology for terabyte bandwidths in the network.
Our USP: While OEMs often only offer limited service levels, we secure your systems with full service, our own SLA model and guaranteed 4-hour recovery time.
Another advance is NVLink Fusion, which enables heterogeneous systems by connecting GPUs, CPUs or other accelerators from different manufacturers with extremely low latency. At the same time, CXL (Compute Express Link) is gaining in importance, as memory can be shared across multiple components and used more flexibly.
New possibilities are also emerging for storage: Peer-to-peer SSDs with a direct connection to GPUs bypass the CPU and drastically reduce latencies. The latest XL flash models deliver up to 10 million IOPS and maximize data throughput for training and inference workloads. Complemented by NVMe SSDs with GPUDirect Storage and parallel file systems such as Lustre, BeeGFS or GPFS, a highly scalable storage architecture is created that can supply thousands of GPUs simultaneously.
Our promise: We advise you on the selection of the right combination of compute, storage and networking – independent of manufacturer and cost-effective. With GPU trade-ins (also for defective cards) and customized service packages, we extend the life cycle of your hardware and protect your investment in the long term.
Then talk to us about a customized solution – manufacturer-independent, economical and tailored to your IT landscape.
Then talk to us about a customized solution – manufacturer-independent, economical and tailored to your IT landscape.
Rethinking infrastructure
GPU clusters require extreme energy density and innovative cooling. Direct liquid cooling or immersion cooling have long been standard. We plan and support these high-density environments – including maintenance of pumps, pipes and heat exchangers.
Intelligent monitoring shows consumption, efficiency and thermal load in real time. This not only ensures performance, but also your operating costs.
Maintenance & operational reliability for your AI data center
GPU clusters operate permanently under extreme conditions: high temperatures, enormous power consumption and complex network loads. Without a consistent maintenance strategy, companies risk failures, performance losses and rising operating costs. Added to this is the fast hardware cycle of modern GPUs – without proactive firmware and lifecycle management, systems lose value early on. Cooling and energy systems also need to be checked regularly, as even the smallest defects can have serious consequences for stability. End-to-end monitoring combined with predictive maintenance is therefore essential to ensure availability and reduce total cost of ownership in the long term.
Our approach:
- Third-party hardware maintenance incl. GPU health checks, firmware updates and function tests
- Full service for servers & GPUs with SLA and 4h recovery time
- Predictive maintenance through continuous sensor data evaluation
- Transparency through technical & economic reporting
Answers to key questions about AI in the data center
What software is needed to operate an AI data center?
An AI data center requires more than just hardware. It can only be used efficiently through software orchestration. Classic HPC environments rely on Slurm as a scheduler, while containerized AI workloads are usually orchestrated with Kubernetes. In addition, NVIDIA Base Command offers special functions for GPU monitoring, resource management and reporting.
Why is an MLOps infrastructure so important?
MLOps forms the bridge between the development and operation of AI models. Automated pipelines for training and inference, CI/CD processes for machine learning and monitoring of models in production ensure that models work reproducibly, reliably and efficiently. Without MLOps, there is a risk of inconsistencies, inefficient processes and longer time-to-market.
How can the efficiency of AI workloads be optimized?
Efficiency gains are achieved through GPU scheduling, which maximizes utilization, and energy-adaptive workloads, which dynamically adjust power consumption. Automated scaling of resources also helps to ensure that computing capacity is only used when it is actually needed.
What role does monitoring and reporting play in operations?
Monitoring ensures that technical key figures such as GPU utilization, memory bandwidths or network performance are visible at all times. We supplement this with reporting that also prepares economic key figures – such as costs per training or efficiency metrics for management. This makes the infrastructure not only technically transparent, but also economically.
Can existing IT teams operate these software solutions themselves?
In principle, yes, but operating an AI infrastructure requires experience with HPC, Kubernetes and MLOps frameworks. Many companies reach their limits here, as the relevant expertise is in short supply on the market. We provide support with consulting, train internal teams and offer monitoring and maintenance services to ensure that operations run smoothly.
Your contact person
Rainer WaiblingerCTO
There is a clever solution for every technical challenge - let us advise you and find the optimum solution.
