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AI servers for training, inference, and deployment are purpose-built systems for building, running, and scaling machine learning workloads. They fit teams working with AI, data science, and production ML, from startups to enterprise R&D.
The platform has several possible configurations of GPU servers using Nvidia GPUs ranging from low cost to professional Tesla class cards. Each of the servers comes with preinstalled software for AI, ML, and data science as well as ready-to-use multimodal chatbot solutions for quicker deployment.
Order a server with pre-installed software and get a ready-to-use environment in minutes.
Open source LLM from China - the first-generation of reasoning models with performance comparable to OpenAI-o1.
Google Gemma 2 is a high-performing and efficient model available in three sizes: 2B, 9B, and 27B.
New state of the art 70B model. Llama 3.3 70B offers similar performance compared to the Llama 3.1 405B model.
Phi-4 is a 14B parameter, state-of-the-art open model from Microsoft.
PyTorch is a fully featured framework for building deep learning models.
TensorFlow is a free and open-source software library for machine learning and artificial intelligence.
Apache Spark is a multi-language engine for executing data engineering, data science, and machine learning on single-node machines or clusters.
Open ecosystem for Data science and AI development.
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Modern AI workloads are GPU server based, as GPU is designed with massive parallel computation. They dramatically speed up training and inference of neural networks, handle large models efficiently, and make real-time AI services practical. For LLMs, computer vision and multimodal models, CPUs alone are normally a bottleneck.
A dedicated GPU server is more than a home GPU set up and provides stable power usage, proper cooling, high memory capacity, fast networking and 24/7 reliability. It is made for continuous heavy workloads, remote access and scaling while a home GPU is usually capped by thermal limitations, uptime, bandwidth and risk of operation. This is based on infrastructure design principles as well as common production experience.
Servers are shipped with preinstalled and pre-configured software for AI, ML, and data science. You can immediately start training or running models without manually setting it up. Based on common practices of managed GPU infrastructure.
Multiple GPU classes exist from high-end RTX classes to professional-grade Tesla GPUs for large-scale training and inference. This makes it possible to select the appropriate balance between cost and performance. Based on segmentation from Nvidia GPU and real world workloads.
The infrastructure is built on standard ML frameworks and open tools, models and code can be migrated without dependency on proprietary platforms or APIs. Based upon use of common industry stacks.
Only tested and stable versions of large language models are provided, that can be used in production rather than in experimental builds. This helps to reduce operational risk. Based on version pinning practices in ML operations.
All the data is handled on dedicated servers and is not shared with third parties. This is important for sensitive data and compliance requirements. Based on single tenant infrastructure design.
Pricing is clear and predictable and there are no hidden fees. Costs are easy to comprehend and prepare for. Based on fixed and usage models pricing.
Flexible billing options enable short-term experimentation or long running production workloads, helping control infrastructure costs. Based on standard cloud billing models.
Servers are normally provisioned within about 15 minutes after the order is confirmed. Based on automated provisioning workflows.
Choose the graphics setup which will suit your performance and budget requirements.
Pick a preconfigured LLM or an application for machine learning, data science, or AI workloads.
We are provisioning the server and set up ready-to-use environment automatically.
Run training jobs, perform inference, or work with LLMs and chatbots right away.
No unexpected model updates
You decide when and how models are updated.
Data stays on your server
All processing happens inside your infrastructure, without external data transfer.
Unlimited tokens and requests
No per-token limits imposed by third-party APIs.
API-ready environment
Models and tools are available via standard APIs for easy integration.
High bandwidth (1–10 Gbps)
Supports fast data transfer and low-latency access.
NVMe SSD storage
High-speed storage for datasets, checkpoints, and embeddings.
Easy scaling
Upgrade GPU, memory, or storage as your workload grows.
Predictable costs
Fixed infrastructure costs instead of usage-based billing.
Custom environments
Install your own libraries, drivers, and tooling.
Better performance consistency
No shared-rate limits or noisy neighbors typical of public APIs.
Powerful language models to run chat, assistants, and internal applications in large scale with full control and predictability.
Training models or refining the existing models on personal data without exposing them to external data.
Implement autonomous or semi-autonomous workflow, monitoring and task executing agents.
Generate, refactor and analyze development pipelines by use of coding models.
Create images or video material with the help of diffusion or multimodal models on specific GPUs.
Accelerate the use of a laptop-based processor with a GPU to process large datasets, execute experiments, and create ML pipelines.
Millions of operations can be processed per second by AI servers specifically designed for AI and machine learning tasks together with high-speed GPUs and optimized AI software programs.
Customers gain maximum performance together with reliability and scalability from dedicated AI servers because these systems operate independently of other users.
The company provides AI servers which include NVIDIA RTX 4090, 5090, Tesla A100, H100 models.
Users can select their server and software before finishing their order thus gaining immediate access to their system.
Our AI servers operate under highly secure conditions since they implement enterprise-level security practices and data encryption alongside non-stop monitoring efforts.
The deployment time for AI servers reaches minutes which enables you to begin your work right away.
The platform supports TensorFlow together with PyTorch along with JAX and multiple significant AI frameworks.
Get Started with an AI Server Today Launch your own AI infrastructure in minutes and stay in full control of performance, data, and costs.
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