Lambda Labs (GPU Cloud) Connector for AI agents.
7 live capabilities
Manage GPU cloud infrastructure and ML training workloads.
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Why people use Lambda Labs (GPU Cloud)
Lambda Labs (GPU Cloud) for Faster GPU Provisioning
This Connector lets you do all of that in a single chat window. You just tell your agent to get a box ready, and it handles the inventory check and provisioning for you. You get a ready-to-use connection string without ever opening a second tab.
What Vinkius changes
You get a conversational interface for your entire Lambda Labs GPU fleet.
Use it from Claude, ChatGPT, Cursor or another AI client you already have.
One account · 5,900+ Connectors
- Real-world use case 01
Quickly spinning up a training box
An engineer needs an H100 for a 4-hour run.
- Real-world use case 02
Checking regional availability
A researcher wants to find the cheapest A100s.
- Real-world use case 03
Cleaning up idle compute
An admin wants to stop costs.
Complete set · 7capabilities
The complete Lambda Labs (GPU Cloud) capability set.
These are the exact actions your AI can choose when you ask it to work with Lambda Labs (GPU Cloud).
01—04
4 capabilities in this set.
Part of 7 available through Lambda Labs (GPU Cloud).
- 01 Capability
Terminate instances
Permanently destroy Lambda GPU instances to stop billing instantly. Use this to clean up your cloud footprint.
- 02 Capability
List instances
List all currently running GPU instances on your Lambda Cloud account. This gives you a quick overview of your active fleet.
- 03 Capability
Get instance
Get exact hardware details and the SSH connection string for a specific instance. This makes jumping into your terminal much faster.
- 04 Capability
Launch instance
Provision a new Lambda GPU virtual machine with specific hardware and SSH keys. This gets your training environment ready quickly.
05—07
3 capabilities in this set.
Part of 7 available through Lambda Labs (GPU Cloud).
- 05 Capability
List instance types
Discover available Lambda GPU instance specifications and regional pricing. This helps you find the best hardware for your budget.
- 06 Capability
List ssh keys
Enumerate all globally managed public SSH keys in your Lambda account. This ensures you have secure access to your remote boxes.
- 07 Capability
List filesystems
Map persistent shared NAS volumes within the Lambda ecosystem. Use this to manage shared data across multiple workers.
Set up in minutes
One URL. Then ask Lambda Labs (GPU Cloud) to work.
Claude and ChatGPT only need the Connector URL. Copy it once, add it in settings, and use Lambda Labs (GPU Cloud) from the conversation.
Choose your client
Live previewAdvanced clients IDE · CLI
Claude · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_GornvRH1C8hhZ2KJ4gLtkhB2ck8RuwNlliqUvB8m/mcp - Step 01
Open Connectors
In Claude Web or Claude Desktop, open Settings and choose Connectors.
- Step 02
Add the URL
Choose Add custom connector, name it Lambda Labs (GPU Cloud), and paste the URL above.
- Step 03
Turn it on in chat
Select +, open Connectors, and enable Lambda Labs (GPU Cloud) for the conversation.
ChatGPT · Web + desktop
Connector URL · ready to paste
Streamable HTTPhttps://edge.vinkius.com/vk_preview_GornvRH1C8hhZ2KJ4gLtkhB2ck8RuwNlliqUvB8m/mcp - Step 01
Open MCP settings
On desktop, open Settings and MCP servers. On web, open your workspace app or connector settings.
- Step 02
Add the URL
Choose Add server with Streamable HTTP, or create a custom MCP app, then paste the Lambda Labs (GPU Cloud) URL.
- Step 03
Save and start
Save the connection and enable Lambda Labs (GPU Cloud) in your conversation. Desktop may ask you to restart once.
Cursor · IDE configuration
Advanced setup
{
"mcpServers": {
"lambda-labs-gpu-cloud": {
"url": "https://edge.vinkius.com/vk_preview_GornvRH1C8hhZ2KJ4gLtkhB2ck8RuwNlliqUvB8m/mcp"
}
}
}- Step 01
Open MCP Settings
Press Cmd+Shift+P (macOS) or Ctrl+Shift+P (Windows/Linux) → search "MCP Settings"
- Step 02
Add the server config
Paste the JSON configuration above into the mcp.json file that opens
- Step 03
Save the file
Cursor will automatically detect the new Connector
- Step 04
Start using Lambda Labs (GPU Cloud)
Open Agent mode in chat and ask: "Using Lambda Labs (GPU Cloud), help me...". 7 tools available
VS Code Copilot · IDE configuration
Advanced setup
{
"mcpServers": {
"lambda-labs-gpu-cloud": {
"url": "https://edge.vinkius.com/vk_preview_GornvRH1C8hhZ2KJ4gLtkhB2ck8RuwNlliqUvB8m/mcp"
}
}
}- Step 01
Create MCP config
Create a .vscode/mcp.json file in your project root
- Step 02
Add the server config
Paste the JSON configuration above
- Step 03
Enable Agent mode
Open GitHub Copilot Chat and switch to Agent mode using the dropdown
- Step 04
Start using Lambda Labs (GPU Cloud)
Ask Copilot: "Using Lambda Labs (GPU Cloud), help me...". 7 tools available
Windsurf · IDE configuration
Advanced setup
{
"mcpServers": {
"lambda-labs-gpu-cloud": {
"url": "https://edge.vinkius.com/vk_preview_GornvRH1C8hhZ2KJ4gLtkhB2ck8RuwNlliqUvB8m/mcp"
}
}
}- Step 01
Open MCP Settings
Go to Settings → MCP Configuration or press Cmd+Shift+P and search "MCP"
- Step 02
Add the server
Paste the JSON configuration above into mcp_config.json
- Step 03
Save and reload
Windsurf will detect the new server automatically
- Step 04
Start using Lambda Labs (GPU Cloud)
Open Cascade and ask: "Using Lambda Labs (GPU Cloud), help me...". 7 tools available
Cline · IDE configuration
Advanced setup
{
"mcpServers": {
"lambda-labs-gpu-cloud": {
"url": "https://edge.vinkius.com/vk_preview_GornvRH1C8hhZ2KJ4gLtkhB2ck8RuwNlliqUvB8m/mcp"
}
}
}- Step 01
Open Cline MCP Settings
Click the Connectors icon in the Cline sidebar panel
- Step 02
Add remote server
Click "Add Connector" and paste the configuration above
- Step 03
Enable the server
Toggle the server switch to ON
- Step 04
Start using Lambda Labs (GPU Cloud)
Ask Cline: "Using Lambda Labs (GPU Cloud), help me...". 7 tools available
Claude Code · Terminal command
Advanced setup
claude mcp add lambda-labs-gpu-cloud --transport http "https://edge.vinkius.com/vk_preview_GornvRH1C8hhZ2KJ4gLtkhB2ck8RuwNlliqUvB8m/mcp"- Step 01
Install Claude Code
Run npm install -g @anthropic-ai/claude-code if not already installed
- Step 02
Add the Connector
Run the command above in your terminal
- Step 03
Verify the connection
Run claude mcp to list connected servers, or type /mcp inside a session
- Step 04
Start using Lambda Labs (GPU Cloud)
Ask Claude: "Using Lambda Labs (GPU Cloud), show me...". 7 tools are ready
Where the request belongs
Work Lambda Labs can move forward.
This is for the ML engineer who's tired of clicking through complex cloud dashboards at 2 a.m. to find an available H100. It's for anyone who needs to manage high-performance compute without the overhead of manual provisioning.
ML Engineer
Launches GPU boxes for fine-tuning models during late-night research sessions.
Data Scientist
Retrieves Jupyter Lab tokens and checks node status without switching windows.
Infrastructure Ops
Manages shared NAS volumes and SSH keys across a fleet of worker nodes.
Build the capability set
Add more capabilities.
Each Connector adds new actions and data without changing how you work.
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Paperspace
Provision and track powerful GPU workloads via Paperspace. list compute instances, fetch active deployments, trace team projects, and query Gradient environments via AI.
Modal (Serverless AI Infrastructure)
Manage serverless compute via Modal. audit active apps, track GPU deployments, and monitor network volumes.
Vast.ai (GPU Rental Cloud API)
Rent high-performance GPUs for AI and deep learning. Search marketplace offers, deploy Docker containers, and manage your cloud GPU fleet.
Linode (Akamai)
Manage Linode cloud infrastructure—provision compute instances, manage Kubernetes clusters (LKE), and monitor account details directly from your AI agent.
Bring your own AI
Change the model, client or framework. Keep Lambda Labs connected.
-
Claude -
ChatGPT -
Gemini -
Cursor -
VS Code -
Windsurf -
ZCode -
Cline -
Zed -
Continue -
Kiro -
Roo Code -
Zencoder -
Goose -
Void -
Augment Code -
Amp -
Qodo -
Tabnine -
Pieces -
Sourcegraph Cody -
JetBrains -
Warp -
Amazon Q -
Antigravity -
BoltAI -
Raycast -
Jan -
LM Studio -
AnythingLLM -
Open WebUI -
Msty -
Cherry Studio -
LibreChat -
TypingMind -
Chorus -
5ire -
n8n -
LangChain -
LlamaIndex -
CrewAI -
Vercel AI SDK
Before you connect
Questions about Lambda Labs.
The practical details behind the request, access and result.
Can I use Lambda Labs (GPU Cloud) MCP to launch H100s?
Yes, it lets you provision high-end hardware like H100s or A100s directly through your agent.
Does Lambda Labs (GPU Cloud) MCP help me save money?
It does by making it easy to use the termination capability to stop billing on nodes you aren't using.
Can I see which regions have GPUs available?
Yes, you can ask the Connector to list instance types to see regional availability and pricing.
How do I get my SSH keys with Lambda Labs (GPU Cloud) MCP?
You can use the key listing capability to see all your public keys managed in your account.
Can I find shared storage using Lambda Labs (GPU Cloud) MCP?
Yes, it lets you map out persistent shared NAS volumes in the Lambda ecosystem.
Can I launch a high-performance H100 instance through my agent?
Yes. Use the launch_instance capability and specify the type (e.g. gpu_1x_h100) and region. Your agent will also allow you to attach registered SSH keys so the instance is securely accessible immediately upon boot.
How do I retrieve the Jupyter Lab access token for a running node?
Use the get_instance capability with your Instance ID. Your agent will fetch the complete telemetry, including the public IP and the Jupyter Lab access token if the environment is configured to provide it.
Can my agent check for GPU availability across different regions?
Absolutely. The list_instance_types capability queries the cloud boundary for hardware inventory. Your agent will report which GPU node types are currently available and in which physical regions they are hosted.
One connection away
Give your agent a direct line to Lambda Labs.
Connect Lambda Labs once. Keep it beside 5,900+ managed Connectors when the next task needs more.
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