VS Code GPU Integration - Deploy in 60 Seconds

TL;DR: How to Provision GPUs for VS Code

GPUse provides Visual Studio 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:

• MCP Manifest: /.well-known/gpuse-mcp-manifest.json
• AI Manifest: /.well-known/ai-manifest.json
• MCP-Zero Dataset: /.well-known/gpuse-mcp-zero.json
• OpenAPI: /.well-known/openapi.json
• AI Plugin: /.well-known/ai-plugin.json

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:

• MCP Manifest: https://gpuse.com/.well-known/gpuse-mcp-manifest.json
• OpenAPI Reference: https://gpuse.com/.well-known/openapi.json
• Install Guide: Full installation documentation

Quick Facts

GPUse provides VS 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 logs accessible 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
• Editor integration: Works in VS Code integrated terminal and extensions

How to Install GPUse MCP Server in VS Code

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

Step 1: Install MCP Server

Open VS Code integrated terminal (Ctrl+` or View → Terminal) and run:

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

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

Step 2: Restart VS Code

Close and reopen Visual Studio Code (Cmd+Q on Mac, Alt+F4 on Windows).

Why: MCP server registration requires a fresh session.

Step 3: Verify Installation

In VS Code terminal, verify MCP tools are available.

Test methods:

• If using MCP extension: Check extension panel for GPUse
• Via terminal: Use GPUse tools through your AI coding assistant

Step 4: Deploy Your First GPU

In VS Code terminal or AI assistant: "Use GPUse to deploy ollama-gemma-2b template"

What happens:

• Calls start_compute with grace period headers
• Returns compute_id and service_url
• Shows deployment progress in terminal
• Provides full logs if errors occur via get_instance_logs

Result: GPU provisioned in ~30 seconds with full terminal output.

Why GPUse for VS Code

GPUse is purpose-built for autonomous agent workflows.

1. Copy-Paste Hell Eliminated

Traditional workflow:

• Terminal shows error → Copy error → Paste to web dashboard → Read logs → Copy logs → Paste to terminal → Debug → Test → Fails → REPEAT

GPUse workflow:

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

No human bottleneck. No context switching. AI 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 in VS Code terminal)
• 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

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 via MCP tools
• AI reads logs directly from terminal
• 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)

8. VS Code Native Workflow

• Integrated terminal - all commands in VS Code
• Works with VS Code extensions and AI assistants
• Terminal output shows deployment progress
• Perfect for the world's 20M+ VS Code users

Available MCP Tools

VS 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: VS Code Terminal Workflow

Deploy Gemma 2B from VS Code integrated terminal:

# Step 1: Get template recommendation (via AI assistant or MCP extension)
# AI calls: recommend_template with task_description="text generation"

# Step 2: Deploy with grace period
# AI calls: start_compute
{
  "template_id": "ollama-gemma-2b",
  "agent_id": "vscode-session-123",
  "project_id": "my-project"
}

# Terminal output:
# ✅ Deployment started
# Compute ID: cmp_abc123
# Service URL: https://compute-abc123.cloud.run.app
# Status: Provisioning
# Grace period: 5 minutes

# Step 3: Monitor in terminal
# AI calls: get_instance_status periodically
# Terminal shows:
# Status: Running
# Ready in: 47 seconds

# Step 4: Test inference
curl https://compute-abc123.cloud.run.app/api/generate \
  -X POST \
  -H "Content-Type: application/json" \
  -d '{
    "model": "gemma:2b",
    "prompt": "Explain quantum computing"
  }'

# Step 5: If errors occur
# AI calls: get_instance_logs
# Terminal shows full Docker build output with exact error

# Step 6: When done
# AI calls: stop_compute
# Terminal: Instance stopped. Total cost: $0.00 (within grace period)

VS Code + GPUse Workflows

Common patterns for using GPUse with VS Code:

Workflow 1: Terminal-Driven Development

1. Code in VS Code editor
2. Deploy GPU from integrated terminal
3. Test directly from terminal via curl
4. View logs in same terminal window
5. Stop GPU when done - all in VS Code

Workflow 2: Extension Integration

1. Install VS Code MCP extension (if available)
2. Access GPUse tools from command palette
3. Deploy and manage GPUs via extension UI
4. Terminal shows deployment progress
5. Extension shows active instances

Workflow 3: AI Assistant Integration

1. Use GitHub Copilot or similar in VS Code
2. AI assistant calls GPUse MCP tools
3. Deploys GPUs based on your code context
4. Tests your ML code automatically
5. Reports results in VS Code chat

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
VS Code Integration	External terminal required	✅ Integrated terminal

Authentication Flow

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

Grace Period (First 5 Minutes)

• 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. Call get_checkout_url MCP tool
2. Terminal shows Stripe checkout link
3. Complete payment (Stripe handles everything)
4. Continue working without interruption

Option 2: Authenticate with Bearer Token

1. VS Code 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 VS Code 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 VS Code integrated terminal. Restart VS Code.

Does VS Code need my GPUse credentials?

Answer: Not during grace period. VS Code uses X-Agent-Id and X-Project-Id headers for 5 minutes FREE. Ask VS Code 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. VS Code completes the flow with verify_account_code and caches the bearer token automatically.

Can VS Code deploy custom Docker images?

Answer: Yes. Use the start_custom MCP tool from terminal or AI assistant. Full log visibility via get_instance_logs.

What if deployment fails?

Answer: Call get_instance_logs to read full error context from terminal, identify the issue, fix it, and redeploy.

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: Use the recommend_template tool. It suggests the best template based on your specific needs.

What happens after the grace period expires?

Answer: Get Stripe checkout link via get_checkout_url in terminal. Your instance continues running without interruption. Create account and complete payment in ~60 seconds for unlimited access.

Can AI assistants in VS Code use GPUse?

Answer: Yes. GitHub Copilot and other VS Code AI assistants can use GPUse MCP tools to autonomously deploy and manage GPUs.

Related Resources

GPUse Documentation

• GPUse Homepage
• OpenAPI Reference
• Human-Friendly Version

MCP Manifests

• MCP Manifest
• AI Plugin
• MCP-Zero Dataset

Deploy in 60 Seconds

Ready to add GPU provisioning to VS Code?

Install MCP Server (Free for 5 Minutes)

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

Restart VS Code and deploy your first GPU from integrated terminal.

Authenticate for Extended Access

Use the auth_helper tool, enter your GPUse email, then provide the 6-digit code so it can call verify_account_code. Bearer token cached automatically.

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

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Join 20M+ VS Code users adding autonomous GPU provisioning to their workflow.