Claude Code GPU Integration - Deploy in 60 Seconds

Add GPUse MCP server to Claude Code for autonomous GPU provisioning with 5-minute grace period. GPUse MCP provides 16 tools for complete GPU lifecycle management.

Last Updated: November 17, 2025
Install MCP Server

TL;DR: How to Provision GPUs for Claude Code

GPUse provides Claude Code with autonomous GPU provisioning through 16 MCP tools.

  • 5-minute grace period: Test without account; agents continue while humans handle payment
  • Full logging: Complete Docker build + runtime logs (via MCP tools) enable autonomous debugging
  • 16 MCP tools: Complete lifecycle coverage from discovery to shutdown

Machine-Readable Resources

Access GPUse manifests and datasets for autonomous agent integration:

Get Started in 60 Seconds

Copy this one line to your coding agent:

npx -y gpuse-mcp-server@latest configure --force

Your agent handles the rest - no manual configuration needed.

Resources:

Quick Facts

GPUse provides Claude Code with autonomous GPU provisioning through 16 MCP tools.

  • Setup time: 60 seconds (one command)
  • Grace period: 5 minutes FREE without account creation
  • MCP tools: 16 tools for complete GPU lifecycle
  • Logging: Full Docker build + runtime logs visible to Claude via get_instance_logs
  • Cost: $0.73/hour with per-second billing and auto scale-to-zero
  • Templates: 9 battle-tested production templates that just work
  • Provider abstraction: One unified interface for multiple GPU providers

How to Install GPUse MCP Server in Claude Code

GPUse MCP integration enables Claude Code to provision GPUs autonomously in ~30 seconds.

Step 1: Install MCP Server

npx -y gpuse-mcp-server@latest configure --force

What happens: Installer configures Claude Code (and all supported MCP clients) to use GPUse tools. Takes ~30 seconds.

Step 2: Restart Claude Code

Close and reopen your Claude Code CLI session.

Why: MCP server registration requires a fresh session.

Step 3: Verify Installation

Run /mcp command in Claude Code.

Result: You should see gpuse server listed with 16 available tools.

Step 4: Deploy Your First GPU

Ask Claude: "Use the recommend_template tool to suggest a GPU template, then deploy it"

What Claude does:

  • Calls recommend_template MCP tool
  • Suggests appropriate template based on your needs
  • Calls start_compute with grace period headers
  • Returns compute_id and service_url
  • Shows full logs if errors occur via get_instance_logs

Result: GPU provisioned in ~30 seconds. Claude has full access to logs for autonomous debugging.

Why GPUse for Claude Code

GPUse is purpose-built for autonomous agent workflows.

1. Copy-Paste Hell Eliminated

Traditional workflow:

  • Claude asks for logs → You SSH into instance → Copy logs → Paste to Claude → Claude suggests fix → You implement → Test → Fails → REPEAT

GPUse workflow:

  • Claude reads logs directly via get_instance_logs → Autonomous debugging → DONE

No human bottleneck. No context switching. Claude fixes errors independently.

2. Real Pain Timeline vs GPUse Speed

Traditional GPU setup timeline (reality):

  • Account + billing setup: 1 hour
  • IAM permissions: 2-4 hours (most developers fail first try)
  • Learning provider API: 2-3 hours reading docs
  • First successful deployment: 5-10 failed attempts (4-6 hours)
  • Total: 1-2 days (best case) to 1 week (typical case)

GPUse timeline:

  • Install: 60 seconds (one command)
  • First deployment: 60 seconds
  • Total: 2 minutes

3. Battle-Tested Templates

  • 9 production templates that just work
  • First-try success vs iteration hell elsewhere
  • Pre-configured environments (no DIY dependencies)
  • Ever-expanding access to more GPUs and templates via same MCP tools

Templates include: Echo Server (Test), Ollama Gemma 2B (Lightweight), Ollama Gemma3 4B (Multimodal), Ollama Llama 3.2 3B (Edge-Optimized), Ollama Mistral 7B (High Quality), Ollama Gemma 7B (Google's Latest), Ollama Gemma3n 4B (e4b) (Efficient 8B→4B), Ollama Qwen2.5-VL 7B (Vision + Text), Whisper Large v3 (Audio Transcription).

4. Provider Abstraction

  • One unified interface for multiple GPU providers
  • No need to learn provider-specific complexity
  • Same MCP tools and APIs - ever-expanding GPU access
  • Switch providers with one parameter change (coming Q1 2026)

5. Grace Period = Live Testing Before Payment

Not just "5 minutes FREE" - it's revolutionary:

  • Test with REAL GPU endpoint BEFORE account creation
  • Real inference calls, real performance testing, real quality validation
  • Then decide to upgrade via Stripe checkout
  • No other platform allows live testing before payment

Claude can complete entire POCs during grace period.

6. Stripe-Only Signup (60 Seconds)

Traditional platforms:

  • Create account, verify email, set up billing
  • Configure IAM permissions and quotas
  • Add payment method, wait for approval
  • Total: 1-2 days (best case) to 1 week (typical)

GPUse:

  • Name + Card + Terms = Done
  • Auto-account linking (Stripe payment creates GPUse account)
  • No IAM setup, no permissions complexity
  • Total: 60 seconds

7. Autonomous Debugging with Full Logs

  • Full Docker build + runtime logs accessible to Claude via MCP tools
  • Claude fixes errors without human intervention
  • Real-time log streaming for agent self-service
  • Zero copy-paste bottlenecks

Other platforms: "Container failed to start" (no details) GPUse: "Step 3/5 failed: pip install transformers==4.35.0 - version not found" (actionable)

MCP-Native Design

  • 16 MCP tools covering full GPU lifecycle
  • No manual API calls needed
  • Claude manages provisioning, monitoring, stopping autonomously
  • Built specifically for AI agents, not humans

Available MCP Tools

Claude Code has access to these 16 GPUse MCP tools:

Template Discovery (3 tools)

  • recommend_template - AI-powered GPU + template recommendation based on your task
  • list_templates - Browse available templates
  • describe_template_endpoints - Provides exact request/response instructions once the template is running

Compute Lifecycle (4 tools)

  • start_compute - Deploy GPU with managed template
  • start_custom - Deploy custom Docker build
  • list_instances - List running instances
  • stop_compute - Stop GPU instance

Monitoring (2 tools)

  • get_instance_status - Check deployment status
  • get_instance_logs - View full Docker build and runtime logs

Payment/Billing (3 tools)

  • get_checkout_url - Convert a grace deployment into a paid GPUse account with one Stripe checkout
  • payment_status - Returns paid vs free mode, account balance, checkout link, and bearer token metadata
  • add_account_funds - Add credits to account

Authentication (3 tools)

  • auth_helper - Guides existing users through the magic-link flow and caches the bearer token
  • request_account_code - Emails the 6-digit code (sub-step inside auth_helper)
  • verify_account_code - Confirms the 6-digit code and stores the bearer token (auth_helper sub-step)

Utility (1 tool)

  • update_mcp_server - Update MCP server to latest version

Complete Example: Claude Deploys Gemma 2B

This workflow shows Claude deploying and using a Gemma 2B instance autonomously.

// What Claude does internally when you ask to deploy Gemma 2B

// 1. Get template recommendation
const recommendation = await mcp.call("recommend_template", {
  task_description: "lightweight text generation and chat"
});

// 2. Deploy with grace period (no auth needed)
const deployment = await mcp.call("start_compute", {
  template_id: recommendation.template_id, // "ollama-gemma-2b"
  agent_id: "claude-code-session-123",
  project_id: "my-project"
});

// 3. Monitor deployment
const status = await mcp.call("get_instance_status", {
  compute_id: deployment.compute_id
});

// 4. If running, make inference request
if (status.status === "running") {
  const response = await fetch(`${deployment.service_url}/api/generate`, {
    method: "POST",
    body: JSON.stringify({
      model: "gemma:2b",
      prompt: "Explain quantum computing in simple terms"
    })
  });
}

// 5. If errors, get logs for debugging
if (status.status === "failed") {
  const logs = await mcp.call("get_instance_logs", {
    compute_id: deployment.compute_id
  });
  // Claude reads logs and fixes errors autonomously
}

// 6. Check payment status and surface checkout if needed
const paymentStatus = await mcp.call("payment_status", {
  project_id: "my-project"
});

if (paymentStatus.payment_status !== "paid") {
  console.log(`Complete payment at: ${paymentStatus.checkout_url}`);
}

Claude Code + GPUse Workflows

Common patterns for using GPUse with Claude Code:

Workflow 1: Test Multiple Models

  1. Claude calls list_templates to see options
  2. Deploys 2-3 different templates via start_compute
  3. Runs same prompt through all models
  4. Compares outputs and performance
  5. Recommends best model for your use case
  6. Stops unused instances via stop_compute to save costs

Workflow 2: Debug Failed Deployment

  1. Claude deploys custom Docker build via start_custom
  2. Build fails (missing dependency)
  3. Claude calls get_instance_logs to read full error
  4. Identifies pip install transformers==4.35.0 failed
  5. Fixes Dockerfile with correct version
  6. Redeploys successfully via start_custom

Workflow 3: Continuous Deployment

  1. You update code in your repo
  2. Claude detects changes
  3. Builds new Docker image via start_custom
  4. Deploys to production
  5. Monitors logs via get_instance_logs
  6. Rolls back if issues detected

Comparison: GPUse vs Manual GPU Setup

Traditional GPU setup takes days, not hours. GPUse takes 2 minutes.

Pain Point Traditional GPU Setup GPUse MCP
Account creation Manual billing + quotas + IAM approvals 60 sec (Stripe only)
Permissions setup 2-4 hours (IAM roles, policies) None needed
API learning Hours reading provider docs Natural language
First working instance 5-10 failed attempts (4-6 hrs) First try success (60 sec)
Log access Copy-paste to agent manually Agent reads directly
Debugging Human bottleneck for every error Agent fixes autonomously
Testing Must fund account first 5 min FREE with real GPU
Platform knowledge Learn each provider's complexity Abstracted - one interface
Future expansion Re-learn for each new provider Same MCP tools, more GPUs
Total time to first success 1-2 days (best) to 1 week (typical) 2 minutes
CLI Integration Manual API calls required ✅ Native MCP support in Claude Code

Authentication Flow

To use GPUse beyond the 5-minute grace period, Claude uses the full auth flow:

Grace Period (First 5 Minutes)

  • Claude uses X-Agent-Id and X-Project-Id headers
  • No authentication required
  • FREE compute for 5 minutes per project

Extended Access (After Grace Period)

Option 1: Complete Payment

  1. Claude calls get_checkout_url MCP tool
  2. Shows you Stripe checkout link
  3. You complete payment (Stripe handles everything)
  4. Claude continues working without interruption

Option 2: Authenticate with Bearer Token

  1. Claude runs the auth_helper MCP tool and asks for the email tied to your GPUse account.
  2. auth_helper triggers request_account_code and emails you a 6-digit code.
  3. Share the code with Claude so it can finish with verify_account_code.
  4. Bearer token automatically caches across all MCP sessions.
  5. Unlimited GPU access while funds remain

Custom Images: Use start_custom MCP tool to deploy any Docker image with full log visibility.

Common Questions

How do I install the MCP server?

Answer: Run npx -y gpuse-mcp-server@latest configure --force in your terminal. Restart Claude Code. Verify with /mcp command.

Does Claude need my GPUse credentials?

Answer: Not during grace period. Claude uses X-Agent-Id and X-Project-Id headers for 5 minutes FREE. Ask Claude to run the auth_helper MCP tool, enter the email tied to your GPUse account, then provide the 6-digit code it requests via request_account_code. Claude completes the flow with verify_account_code and caches the bearer token automatically.

Can Claude deploy custom Docker images?

Answer: Yes. Use the start_custom MCP tool. Claude can build and deploy custom images with full log visibility via get_instance_logs.

What if deployment fails?

Answer: Claude calls get_instance_logs to read full error context, identifies the issue, fixes it, and redeploys autonomously.

How much does this cost?

Answer: First 5 minutes FREE per project. After that, $0.73/hour for active compute. Auto scale-to-zero means no idle charges. Per-second billing.

Which templates should I use?

Answer: Ask Claude to use the recommend_template tool. It uses AI to suggest the best template based on your specific needs.

What happens after the grace period expires?

Answer: Claude calls get_checkout_url and shows you the Stripe payment link. Your instance continues running without interruption. Create account and complete payment in ~60 seconds for unlimited access.

Can Claude debug errors autonomously?

Answer: Yes. Claude uses get_instance_logs to see full Docker build logs and runtime errors, then fixes issues and redeploys without human help.

How does GPUse compare to RunPod or Modal?

Answer: GPUse provides 60-second setup vs 1-2 days typical for manual GPU providers. Key differences: 5-minute grace period with REAL GPU testing (vs none), full Docker logs accessible to Claude via MCP tools (vs copy-paste required), 16 MCP tools for autonomous orchestration (vs manual API calls), and battle-tested templates that work first try (vs DIY configuration).

Related Resources

GPUse Documentation

MCP Manifests

Deploy in 60 Seconds

Ready to add GPU provisioning to Claude Code?

Install MCP Server (Free for 5 Minutes)

npx -y gpuse-mcp-server@latest configure --force

Restart Claude Code and run /mcp to verify. Ask Claude to use GPUse tools.

Authenticate for Extended Access

Ask Claude to run the auth_helper tool, then provide the 6-digit code from your email so it can finish with verify_account_code. Bearer token cached automatically.

Questions? Email support@gpuse.com or visit gpuse.com.

Join the rapidly growing community of AI agents using GPUse for autonomous GPU orchestration.