JANCTION

Service model

Managed GPU environments for real workloads.

JANCTION is designed around a controlled service path: request, fit check, confirmation, provisioning, access handover, active use, and release.

Prepare request

Requestable options

  • GPU model, performance target, and GPU count
  • Expected start date, duration, region, and latency needs
  • Runtime stack such as CUDA, PyTorch, TensorFlow, JupyterLab, vLLM, ComfyUI, Blender, or Redshift
  • Storage, access, data sensitivity, and isolation requirements

Limitations

  • Environments are provided as GPU containers.
  • Running Docker inside the environment is not supported.

How delivery works

  • The JANCTION team reviews workload fit and capacity availability.
  • Usage terms, lead time, access method, and support boundary are confirmed before use.
  • The environment is prepared, access is handed over, and release is coordinated when the usage period ends.

Service lifecycle

  1. 01Customer interest
  2. 02Fit and package evaluation
  3. 03Terms confirmation
  4. 04Environment provisioning
  5. 05Access handover
  6. 06Active use and support
  7. 07Release and data handling

Questions customers usually ask

Is provisioning instant?

Provisioning timing depends on availability, requested configuration, and access requirements. The JANCTION team confirms lead time before delivery.

Can I request a specific runtime stack?

Yes. Include CUDA, framework, package, notebook, inference server, or application requirements in the request.

How is data handled?

Persistent and temporary storage expectations should be agreed before use. Include data sensitivity and deletion needs in the request.