Powerful AI servers for your toughest workloads
From edge to hyperscale – AI systems from a single source
Your AI journey starts here
MH AI server and data center solutions give companies of all sizes access to high-performance, scalable, and fully integrated AI infrastructures. Whether as a complete system, flexible AI data center cluster, or compact workstation, our solutions are designed for maximum energy efficiency, immediate operational readiness, and future-ready performance. Ideal for challenging applications such as machine learning, deep learning, computer vision, and generative AI.
Our solutions at a glance
Turnkey Systeme
Complete solutions with preinstalled software and AI stack. Ready for immediate use. Perfect for companies that need productive AI infrastructure quickly and reliably—without having to do any integration work themselves.
Typical use cases:
- Real-time inference in video analysis/object detection: Object recognition or facial recognition in security systems, for example to trigger automation via camera feeds. CPUs are often too slow for this – GPUs perform these tasks efficiently and in parallel.
- Generative AI (Stable Diffusion, image synthesis): Small to medium-sized image generators or code generators (e.g., Stable Diffusion) that run on compact GPU hardware solutions.
- Computer vision & sensor fusion in the edge environment: Automated image recognition, video analysis, predictive maintenance, or production monitoring directly on site—in real time without cloud latency.
- Batch inference & feature engineering for ML pipelines: Processing multiple smaller predictions or DNN evaluations in parallel—as in predictive analytics or model batching—offers high GPU utilization.
AI Data Centre
Fully integrated, scalable data centers for high-performance AI applications.
Typical use cases:
- Generative AI (Stable Diffusion, image synthesis): Large image generators or code generators (e.g., Stable Diffusion) that run on scalable GPU solutions.
- ML/AI research or training: Context such as CSV data sets or training large models on distributed GPU infrastructure servers. CPUs help with pre-processing, GPUs with training.
Standalone Hardware
GPU servers, workstations, and components for your own infrastructure.
Typical use cases:
- Local LLM models & chatbots: e.g., llama.cpp or smaller LLMs running on compact hardware—for answering simple text queries or control via chatbot.
- Inference & fine-tuning of smaller language models: Edge inference of NLP models, such as sentiment analysis or smaller language LLMs – particularly efficient on dedicated GPU servers.
Solutions and service from one source.
At MH Service, you not only get powerful AI hardware, but also comprehensive support—from needs to operation. Our teams of experts will guide you through the planning, dimensioning, and rollout of your AI infrastructure—on-premises or in the cloud. Optionally, you can get preconfigured systems with preinstalled models, frameworks, and security components tailored to your use cases.
AI Ready
DGX Station
A powerful desktop AI supercomputer with the Grace Blackwell GB300 Superchip and 784GB unified memory, built for enterprise-grade AI training and inference. Perfect for intensive workloads at your desk.
DGX Spark
The world’s smallest AI supercomputer with the Grace Blackwell GB10, delivering 1 petaflop of performance and 128GB memory for local AI prototyping, fine-tuning, and inference, scalable to data centres or cloud.
Enterprise AI-Server
DGX B200 Server
A powerful AI server with 8 Blackwell GPUs and next-gen NVLink™, offering 3× faster training and 15× better inference. Built for enterprise-scale AI and model deployment.
DGX B300 Server
A high-performance AI system with Blackwell Ultra GPUs, delivering up to 144 PFLOPS inference and 72 PFLOPS training—built for generative AI, reasoning, and seamless data centre integration.
AI Factory
AI Factory (Blackwell, Grace, Bluefield): A next-gen AI infrastructure with powerful GPUs, advanced networking, and efficient design—built to accelerate reasoning and inference at scale across industries.