In the age of generative AI and machine learning, the right computing power is key to a project’s success. At IT-RESELL.COM, we configure GPU-enabled enterprise servers from HPE, DELL, Lenovo and Fujitsu – precisely tailored to whether you are training models, fine-tuning them or running inference in production. Full data sovereignty, predictable costs, certified enterprise hardware.
Buy AI servers – hardware for deep learning and machine learning
GPU servers for training and inference · new and refurbished from leading manufacturers
Which servers would you like to purchase?
Please send us your enquiry or contact our representatives directly.
What to look out for when buying an AI server
GPU architecture, VRAM and PCIe connectivity are key to performance – here are the six most important factors.
It is crucial that the model fits entirely within the GPU memory (VRAM). Modern Tensor Cores massively accelerate AI calculations.
PCIe Gen 4 / Gen 5 with sufficient lanes – to ensure GPUs aren’t held back by the interface. Choosing the right CPU is also a key factor here.
CPU for data pre-processing; ample RAM (256 GB+) to store datasets and keep GPUs working at full capacity.
Fast NVMe as a data tier – training data must be supplied to the GPUs without bottlenecks .
10 GbE or faster to avoid data bottlenecks when loading large datasets.
GPU servers have high power consumption. Redundant high-performance power supplies and adequate rack cooling are essential.
Software ecosystem
NVIDIA CUDA / cuDNN · PyTorch · TensorFlow · JAX · Docker + NVIDIA Container Toolkit · Kubernetes for scalable ML pipelines.
Our hardware recommendations for AI & ML
From inference platforms to multi-GPU training clusters.
| Scenario / Requirement | Recommended Systems | Configuration Recommendation |
|---|---|---|
| Inference / Production | DELL R750 · HPE DL380 Gen 11 · Lenovo SR650 V3 | 1 GPU, 256 GB RAM, NVMe – also very cost-effective as refurbished |
| Fine-tuning / smaller training runs | DELL R760 · HPE DL380 Gen 11/12 · Fujitsu RX2540 M7 | 1–2 GPUs, 512 GB RAM, fast NVMe data tier |
| Deep learning / multi-GPU | DELL R760 · HPE DL380a Gen 12 · Lenovo SR650 V3 (new) | Multiple double-width GPUs, max. RAM, PCIe Gen 5, high-performance PSU |
| Edge inference / decentralised | Lenovo SR630 V2 · Fujitsu TX1330 M5 + inference GPU | Compact, remotely manageable, for sites without a dedicated data centre |
Partner benefit
Enquire now →
As a Dell and Lenovo partner, we receive particularly attractive terms on a project-by-project basis – it’s especially worth making a direct enquiry for GPU projects.
Specific use cases
Natural Language Processing
Language models for text analysis or chat applications are trained and fine-tuned on GPU servers – the data is stored securely in our own data centre, without any reliance on the cloud.
Computer vision in production
An image recognition model runs on a single inference GPU in a refurbished DELL R750 directly on the production floor – cost-effective, fast and fail-safe.
Research & industrial simulation
Several GPU nodes (Dell PowerEdge XE9685L, NVIDIA DGX H200) form a scalable training and simulation cluster, orchestrated via Kubernetes.
Extended life cycle · lower carbon footprint
Why refurbished servers make sense for this application
Refurbished servers are particularly attractive for inference and smaller training tasks: the previous-generation CPU platform is perfectly adequate, and you can invest the savings specifically in the GPU.
Up to 70%
cheaper than new equipment for the same server platform
‘GPU first’ –
reinvest the savings directly into the GPU budget
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Frequently Asked Questions
A highly specialised system with powerful GPUs that massively accelerates parallel computing operations and is designed to train neural networks and process data for machine learning.
A standard server is optimised for sequential CPU tasks. A GPU server, thanks to its architecture, performs thousands of computational operations simultaneously – ideal for AI training, inference, rendering and scientific simulations.
Training – the process of training or fine-tuning models – is extremely GPU-intensive and requires a lot of VRAM and fast data access. Inference – the productive use of trained models – is significantly less demanding. Separating these two avoids costly overspecification.
Primarily the GPU architecture (Tensor Cores) and VRAM, as well as the PCIe connection, fast NVMe storage, sufficient RAM, networking and adequate cooling for continuous operation.
Own hardware provides full data sovereignty, predictable costs and lower latencies. With continuous operation, an in-house server often pays for itself quickly compared to cloud fees. We’d be happy to advise you personally on this wide-ranging topic.
Yes, significantly. Training requires several high-performance GPUs with plenty of VRAM. Inference often runs on a single GPU or even on the CPU – and can be implemented cost-effectively on refurbished hardware.
Yes – particularly for inference and fine-tuning. The previous-generation server platform is sufficient; the budget is channelled specifically into the GPU.
With NVIDIA GPUs: CUDA, cuDNN, PyTorch, TensorFlow, JAX and the NVIDIA Container Toolkit for Docker/Kubernetes.