Gpu Bridge
Expose thirty GPU-backed models and media APIs to Claude Code or Cursor without wiring each vendor yourself.
Overview
GPU-Bridge is an MCP server for the Build phase that exposes thirty GPU-powered AI services—LLM, image, video, audio, and embeddings—as tools your coding agent can call.
What is this MCP server?
- Thirty MCP tools covering LLM, image, video, audio, and embedding workloads on shared GPU infrastructure
- Stdio npm package @gpu-bridge/mcp-server v2.4.3 for local agent attachment
- Auth via GPUBRIDGE_API_KEY (gpub_ prefix) or optional x402 micropayments
- Central bridge at gpubridge.io instead of maintaining separate GPU provider accounts
- 30 GPU-powered AI services exposed as MCP tools
- Server version 2.4.3
- Stdio transport via npm package @gpu-bridge/mcp-server
What problem does it solve?
You need many GPU AI capabilities in your agent but do not want separate vendor integrations or your own GPU ops.
Who is it for?
Solo builders shipping agent features that need multimodal or embedding APIs without self-hosting GPUs.
Skip if: Teams that require on-prem-only inference, air-gapped environments, or fine-grained per-model infrastructure control.
What do I get? / Deliverables
After install, your agent can invoke a broad catalog of inference and media tools through one MCP bridge and one API key.
- Stdio MCP server wired to thirty GPU AI tool endpoints
- Agent-callable LLM, image, video, audio, and embedding operations via one bridge
Recommended MCP Servers
Journey fit
Solo builders wire agent workflows during the build phase when they need multimodal AI capabilities inside the IDE. Integrations is the canonical shelf for MCP servers that proxy external AI and inference APIs into the coding agent.
How it compares
MCP inference gateway with bundled GPU APIs, not a local Ollama-style runtime or a single-purpose image skill.
Common Questions / FAQ
Who is GPU-Bridge for?
Indie and solo developers who use Claude Code, Cursor, or other MCP clients and want one bridge to many GPU AI tools.
When should I use GPU-Bridge?
Use it during build when you are adding LLM, image, video, audio, or embedding calls into agent workflows and prefer a managed GPU backend.
How do I add GPU-Bridge to my agent?
Install @gpu-bridge/mcp-server from npm, set GPUBRIDGE_API_KEY from https://gpubridge.io, and point your agent’s MCP config at the stdio server entry.