Computing resources for your AI workloads
Models need more than GPUs: networking, storage, resource management and workload monitoring all matter. We bring these components together into working infrastructure for your development team and enterprise AI services. The configuration reflects your actual use cases.
What the solution includes
- Assessment of models, data volumes and expected workloads.
- Design of GPU nodes, networking and storage.
- An environment for model training, fine-tuning and inference.
- Resource allocation, user access and monitoring.
From the first task to launch
We define requirements and select the configuration and software environment. We test the infrastructure on agreed tasks, establish operating procedures and provide documentation. Equipment supply and scaling are agreed as part of the project scope.
Questions and answers
Do we have to start with a large cluster?
No. Infrastructure size depends on your tasks and workload. The first stage can use a pilot configuration to test models, data and operating procedures.
Can we use existing hardware?
This is assessed during the initial review. We need to check compatibility with the software environment, model requirements and expected workload, as well as hosting and operating conditions.
Can the system run on our own infrastructure?
On-premises deployment can be built into the project architecture. Data access, network connections, software updates and integration with enterprise systems are agreed separately.
How are computing resources shared between users?
The scope can include job, queue and resource allocation management. Access rules, priorities and limits are defined with your responsible staff.
What determines timelines and costs?
Costs depend on the processes involved, data condition, number of integrations and deployment requirements. After assessment, we agree on stages, acceptance criteria and an estimate. Licences and hardware are priced separately.