Hyperbolic GPU MCP Server

Overview

The Hyperbolic GPU MCP Server bridges the gap between AI assistants and high‑performance GPU resources hosted on Hyperbolic’s cloud platform. By exposing a suite of tools that allow agents to discover, rent, and manage GPU instances—along with SSH connectivity—it gives developers a seamless way to offload compute‑intensive workloads directly from conversational AI workflows. This eliminates the need for manual provisioning or complex cloud console interactions, enabling on‑demand scaling of GPU resources in a single command.

Problem Solved

Modern AI applications often require rapid access to GPUs for training, inference, or data processing. Traditional approaches demand manual account setup, API key handling, and infrastructure management, which can slow development cycles. The Hyperbolic MCP Server automates these steps: it authenticates with a user’s Hyperbolic account, queries the inventory of available GPUs, and orchestrates instance lifecycle operations—all through a simple, declarative interface that an AI assistant can invoke. This reduces friction for developers who want to prototype or deploy GPU‑heavy models without leaving their chat environment.

Core Capabilities

• GPU Discovery – returns a real‑time catalog of all rentable GPUs across Hyperbolic’s clusters, allowing agents to present options based on cost, performance, or location.
• Instance Lifecycle Management – , , and let users spin up, monitor, and tear down GPU nodes with precise control over cluster, node, and GPU count.
• Cluster Insight – provides metadata about a cluster, such as available hardware types and current utilization, enabling smarter decision‑making.
• SSH Integration – establishes a remote shell session to any rented instance, allowing developers to run scripts or launch frameworks directly from the assistant.

Use Cases

• Rapid Model Prototyping – Researchers can request a fresh GPU instance, run training code, and receive results—all within the same conversation.
• Continuous Integration Pipelines – CI/CD workflows can invoke the MCP server to spin up a temporary GPU, execute tests, and clean up automatically.
• Collaborative Workflows – Teams can share GPU resources via the assistant, ensuring that everyone has access to consistent hardware without manual provisioning.
• Cost‑Optimized Compute – By querying cluster details and available GPUs, developers can choose the most economical option for a given workload.

Integration with AI Workflows

The server’s tools are designed to be invoked as standard MCP commands. An assistant can ask a user for parameters, translate the conversation into tool arguments, and return real‑time status updates. Because the server handles authentication internally, developers only need to supply their Hyperbolic API token once—either via environment variables or the Claude Desktop configuration. This tight integration means that AI agents can become full‑blown orchestration engines, turning natural language requests into concrete GPU allocations and SSH sessions without leaving the chat.

Unique Advantages

Hyperbolic’s network of GPUs is geographically diverse and highly optimized for machine learning workloads. By exposing this infrastructure through MCP, the server gives developers instant access to a powerful, pay‑as‑you‑go GPU pool without managing underlying cloud accounts. The combination of on‑demand renting, fine‑grained instance control, and built‑in SSH connectivity makes the Hyperbolic GPU MCP Server a distinctive tool for developers seeking to accelerate AI experiments while keeping operational overhead minimal.