GPU Infrastructure for AI
For teams that train models, run inference, and build AI products.
A model catalog, a Playground for experiments, and a ready-to-use API. Choose a model, test it, and connect it to your application—without setting up the infrastructure yourself.
model catalog
Playground
model deployment
inference
API
streaming
function calling
structured output
monitoring
GPU selection for deployment
The team compares several models in an hour and selects the one that fits their scenario.
Cloud Platform
For startups that need flexible resources to launch and grow a digital product.
Jupyter, VS Code, and GPU containers with ready-to-use compute access. Create environments for developing, training, and fine-tuning models without manually configuring GPU infrastructure.
Jupyter
VS Code
GPU containers
custom Docker environments
GPU access
Python and ML frameworks
experiments
training and fine-tuning
working with your own models
Data Scientists launch Jupyter with the required environment and start training models immediately.
Bare Metal Dedicated Servers
For projects with high performance and isolation requirements.
Visual assembly of agents and workflows, ready-made templates, and integration with corporate systems. AI applications that don't just answer, but also take action.
visual agent builder
workflow
ready-made templates
integrations
tools and APIs
scenario testing
agent deployment
access management
The agent receives a client request, analyzes it, and performs actions within corporate systems.
