GPU cloud services
for AI workloads.
Dedicated GPUs, managed cloud and AI services. Tell us what you need to run, the capacity you expect to use and the support you need. Services are in development; we begin with a technical discussion and pilot scope.
Start a conversation →GPU capacity and managed cloud
The proposed service can cover compute alone or a managed environment. Configuration, location and availability are confirmed for each deployment.
Dedicated GPU capacity
Specify the GPU type, quantity, networking, location and reservation period. Capacity may use company-owned GPUs or reserved partner supply.
Managed cloud
Add the infrastructure operations your workload needs: compute, storage, networking, monitoring and support. The proposal identifies what your team runs and what INTERTIE.ai manages.
AI services
Inference, post-training and model access can be discussed separately or alongside dedicated compute.
Inference and agents
Serve models and run customer-authorized tools. The planned runtime manages workflow state, queues, retries and human intervention, with records of resource use and completed tasks.
Enterprise post-training
Adapt an eligible model using customer-permitted data. Compare task results with the existing model and simpler changes to prompts or routing before deciding whether to train.
Model access and routing
Use a common interface for selected open models and provider APIs. The planned gateway includes provider permissions, spending limits, usage records and fallback rules. Supported models will be listed after testing.
How an agent task runs
The proposed platform connects a customer application to models, tools and the compute needed to complete a task.
- 01
Application
Task instructions, permissions and business rules.
- 02
Model call
A hosted open model or an authorized provider API.
- 03
Tool execution
Approved tools, saved state and queued work.
- 04
Result
Return the outcome to the customer application.
A failed model or tool call follows the agreed retry rules or goes to a person for review before the workflow resumes.
Usage records track model calls and compute use across the task to support evaluation and cost reporting.
Model access within this flow
Open models would run under their licences; closed models would be accessed through the provider’s API and terms. Model selection, usage limits and fallback rules sit at the model-call step. API access does not grant model ownership or training rights.
Pilot evaluation
Start with a workload brief. Our technical team uses it to discuss a pilot, the test data and what a successful result would look like.
Quality
Measure completed tasks, incorrect actions and cases that need human intervention against your current setup.
Performance
Test response time, throughput and concurrency across the full workflow, including tool calls.
Reliability
Check failed calls, retries, recovery and whether the workflow retains the state it needs.
Cost
Include model APIs, GPU and CPU use, storage, networking and operating support when comparing the cost of a completed task.
Scope a customer pilot
Send the workload, models, expected usage, location and timing. We can then discuss the technical fit, pilot scope and data requirements. Capacity, pricing, support and service levels belong in the project proposal.
Questions about the service
Are the services available today?
The platform and services are in development. We are discussing customer requirements and pilots. We confirm supported models, capacity, configuration and delivery timing before agreeing a deployment.
Who is the platform for?
AI companies, agent developers and enterprise teams that need dedicated GPU capacity, infrastructure operations or a specific AI service.
Can we discuss GPU capacity without agent services?
Yes. A dedicated GPU requirement can be scoped separately. Include the GPU type or workload, expected scale, region and reservation period.
What data should we send first?
Start with a non-sensitive description of the task, models and tools, expected usage, location and timing. Discuss confidentiality and access requirements before sharing datasets, credentials or private project documents.
How are compute and facility responsibilities handled?
A deployment may use company-owned GPUs or partner capacity. The project agreements identify the equipment owner and the compute, facility and support responsibilities separately.