$ loading_
Discover GPU nodes, reserve them with signed handles, and dispatch inference tasks through MCP.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "gpu-broker-mcp" yet — see the docs or source repo.
You are a GPU scheduling assistant. First use GPU-broker MCP to discover available GPU nodes, then pick a node that meets memory and load requirements. Reserve it with a signed handle and dispatch a text generation inference request to that node, then return the node details, signed handle, and dispatch result.
Return the available GPU list, selection criteria, the signed handle, assigned node, and a summary indicating whether dispatch succeeded.
Show a two-step flow: first discover and reserve a GPU node using GPU-broker MCP and obtain a signed handle; second, dispatch another inference request using the same signed handle, then summarize nodes and statuses for both steps. Keep it stateless, linked only by the handle.
Output a two-step scheduling log with handle references, target nodes, task status, and whether each step remains traceable under a reserved state.
Developers preparing to launch an inference feature need to identify available GPU capacity, reserve resources, and dispatch requests to suitable nodes, improving launch speed and reliability.
DevOps or engineering teams can use signed handles to handle reservation and dispatch in a stateless way under variable traffic, reducing manual state-sync errors and delays.
It is an MCP tool that lets AI agents discover GPU nodes, reserve resources, and dispatch inference tasks, while using signed handles for stateless reservation management.
Available information only states the core capabilities (discovery, reservation, dispatch, signed-handle flow). For installation, authentication, and runtime details, check the source repository.
It links discover, reserve, and dispatch through a unified MCP interface, and uses signed handles for state handling, which suits AI-agent automation. For exact implementation and comparison details, see the source repository.
Expose local NVIDIA GPU and Rust-to-WASM tools through MCP for sovereign compute.
Manage AI agents, GPU infrastructure, costs, and compliance from one control plane.
Connect cloud LLM agents to edge and IoT hardware via standard MCP tools.
Aggregate multiple MCP servers into a few fixed tools for AI apps.
Manage GPU inventory, VM lifecycle, billing, SSH keys, and setup recipes.
Enforce policy checks on AI tool calls without exposing system credentials.