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Manage CoreHub containers, filesystems, distributed training, and inference via MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Coreshub MCP Server" yet — see the docs or source repo.
Using the Coreshub MCP Server, deploy a model inference service on CoreHub with image myorg/llm-infer:latest, allocate 1 GPU, expose port 8000, and return the service status and access endpoint.
Returns the inference service creation result, running status, resource configuration, and access endpoint.
Using the Coreshub MCP Server, create an EPFS filesystem for training data storage named training-data-vol, then show its mount method, capacity details, and current status.
Returns the filesystem creation result, capacity details, mounting instructions, and status overview.
Using the Coreshub MCP Server, submit a distributed PyTorch training job with 4 nodes, attach an existing data volume, and keep reporting job status until it starts running.
Returns the training job submission result, node configuration, data volume attachment details, and latest job status.
Manage and monitor MCP servers centrally with dynamic configuration and control.
Aggregate multiple MCP servers into a few fixed tools for AI apps.
Manage Kubernetes clusters, resources, backups, and diagnostics using natural language.
Manage GPU inventory, VM lifecycle, billing, SSH keys, and setup recipes.
Use one gateway to lazily spawn and proxy multiple MCP servers.
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