智算云扉

智算云扉 (roughly rendered as smart Computing cloud Portal) is a cloud GPU rental service AImed at AI developers, research teams and companies that need compute without owning hardware. Instead of provisioning physical servers, users spin up cloud container instances with selected GPU specifications, then connect through familiar development tools to run training jobs, inference workloads or creative pipelines. The platform is delivered through a web console, so there is nothing to install locally beyond an SSH or browser-based CLIent.

What it does

The core offering is on-demand GPU capacity packaged as container instances. A user picks a GPU specification and an image environment, attaches stoRAGe, and launches an instance. documentation walks through common deep learning stacks such as PyTorch, Tensorflow and JAX, which reduces the setup work nORMally required before a first experiment can run. Because compute is billed by usage rather than reserved in advance, the service suits short experiments as well as longer training runs.

Key capabilities

• GPU container instances: create instances on demand for training, Inference and AIGC workflows.
• Prepared environments: guidance for popular Frameworks and image configurations.
• Multiple connection methods: JupyterLab, SSH, VS Code and PyChArm are documented, along with file transfer tools such as FileZilla.
• PeRSIstent storage: data and model files can be retained and shared, including options for team collaboration storage.
• developer integration: OpenAPI documentation for compute resources and a ComfyUI API reference for programmatic Workflows.
• Transfer utilities: support for moving datasets and model weights in and out of the environment.

Who it is for

The platform targets indiVidual AI developers, small teams and Enterprises that want elastic GPU access. Typical uses include fine-tuning or training large models, running Deep Learning experiments, serving inference endpoints and operating ComfyUI-based generative image pipelines. Teams that already use container-based workflows should find the environment familiar, while users new to cloud GPUs may need to read the setup guides before launching their first instance.

How to get started

1. Open the official site and register an account.
2. Review the instance documentation to underStand image and GPU options.
3. Create a container instance, choosing a GPU specification and framework image.
4. Attach storage and upload data or models.
5. Connect via JupyterLab, SSH or an IDE such as VS Code.
6. Optionally consult the OpenAPI docs to automate instance management.

Pricing and access

The service is described as pay-as-you-go, with costs tied to the GPU and resources consumed. Specific rates, quotas, trial credits and regional availability are not stated in the source material and may vary, so check the official pricing page before comMITting to a workload. The interface and documentation are primarily in Simplified Chinese, which may be a consideration for international users.