GPU Labs | High-performance GPU clusters investments

Home

Technology

The heart of any AI-driven project is a powerful and reliable compute infrastructure. For this reason, we provide a range of high-performance clusters designed to deliver optimal power and efficiency for demanding workloads.

These clusters are equipped with the latest generation of Nvidia GPUs, delivering industry-leading data processing and analysis speeds. A variety of configurations is available, each tailored to specific technical tasks and business requirements.

Scalability

Clusters can be scaled up or down so that computing power grows together with the needs of your project.

Flexibility

Users can select the configuration that best matches their workload, performance targets, and budget.

Efficiency

Hardware-level optimization ensures maximum performance while keeping energy consumption under control.

Enterprise level

The entire ecosystem is designed from the ground up for demanding enterprise workloads.

Innovations in AI Infrastructure

Our team is dedicated to advancing and implementing cutting-edge AI technologies, ensuring that our clients remain positioned at the forefront of technological progress. We continually invest in research, infrastructure, and innovation to support the evolving needs of the AI ecosystem.

Advanced deep-learning and machine-learning techniques are used to process large volumes of data, enabling models to learn and adapt efficiently. This forms the foundation for intelligent systems capable of self-improvement and real-time decision-making.

The clusters are equipped with specialized computing resources, including graphics processors and neuromorphic chips optimized for AI workloads. These technologies accelerate model training and increase overall performance, making the solutions both powerful and efficient.

The infrastructure actively integrates Internet of Things (IoT) solutions for data collection, gathering information from a wide range of devices and sources. This creates a rich basis for training AI models on diverse, large-scale datasets and improves their ability to analyze and understand the real world.

Recognizing the importance of ethical principles and security in artificial intelligence, the team develops internal standards and guidelines for responsible use of AI technologies. The goal is to build safe and fair systems that benefit society and respect data privacy.

Images generated using Stable Diffusion on Nvidia H100 clusters

AI Models

Explore the advanced AI models trained and evaluated on our high-performance computing clusters. Each model reflects our commitment to innovation, precision, and operational excellence in machine learning.

Pixboost

Pixboost enables high-fidelity image upscaling through a multi-stage processing pipeline. Its optimized latent-space refinement architecture significantly improves noise reduction and output clarity compared with conventional models.

Model description

The base Pixboost model first generates latent representations aligned with the target resolution. In the next phase, a specialized high-resolution module refines these representations using the PixboostEdit method, producing a final output with enhanced detail and image quality.

35 mln. parameters

Cluster: Nvidia T4

Documentary

Documentary is engineered to interpret and process document-based imagery, including invoices and structured text layouts. The model is enhanced using DocVQA datasets to support visual question-answering and document understanding tasks with exceptional accuracy.

Model description

The architecture integrates a Swin Transformer–based image encoder with a BART text decoder. Upon receiving an input image, the encoder converts it into an embedding tensor, which is then processed autoregressively by the decoder to generate structured outputs based on the encoded visual information.

56 mln. parameters

Cluster: Nvidia A10

MaskCraft

MaskCraft generates high-precision object masks from input prompts—such as points, shapes, or regions—and can produce complete segmentation masks for all objects within an image. The system is engineered for accuracy, flexibility, and efficient integration into advanced vision pipelines.

Model description

The model integrates an image encoder that applies cross-attention between image embeddings, point embeddings, and contextualized mask representations. The decoder then synthesizes object masks based on these contextualized embeddings, delivering consistent, high-quality segmentation outputs.

92 mln. parameters

Cluster: Nvidia A10

Meshwork

Meshwork converts a single input image into a fully textured 3D mesh using a specialized convolutional reconstruction framework. This enables the rapid creation of detailed 3D object models directly from 2D visual data.

Model description

The architecture incorporates a diffusion model to generate multi-view images from a single input, a secondary diffusion model to produce CCM mappings, and a UNet-based reconstruction network that assembles the final textured mesh with high fidelity.

140 mln. parameters

Cluster: Nvidia H100