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Documentation Index

Fetch the complete documentation index at: https://docs.antryk.com/llms.txt

Use this file to discover all available pages before exploring further.

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GPU Compute Services

Deploy GPU-powered applications and compute-intensive workloads directly inside Antryk using the built-in GPU Service platform. Antryk GPU Services provide scalable infrastructure for AI/ML workloads, inference engines, training pipelines, rendering systems, data processing applications, and high-performance backend services. The deployment platform allows organizations to connect repositories, configure runtime environments, manage environment variables, and launch production-ready GPU instances with a simplified deployment workflow. Using Antryk GPU Services, teams can:
  • Deploy AI & ML applications
  • Run model training workloads
  • Launch GPU inference services
  • Deploy CUDA-enabled applications
  • Host LLM infrastructure
  • Run rendering pipelines
  • Execute compute-intensive workloads
  • Configure scalable GPU resources
  • Manage deployments from Git repositories
  • Configure build & runtime environments
  • Manage environment variables securely
  • Deploy applications with one-click infrastructure provisioning
This provides organizations with centralized GPU infrastructure management and simplified deployment operations.

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What is GPU Service?

GPU Service is Antryk’s high-performance compute deployment platform designed for AI systems, machine learning workloads, backend GPU services, rendering applications, and infrastructure-heavy compute operations. The platform enables users to deploy applications directly from connected Git repositories while selecting dedicated GPU hardware configurations based on workload requirements. Antryk automatically provisions infrastructure, installs dependencies, builds the application, configures runtime execution, and deploys the service using the selected GPU resources. The platform supports:
  • AI/ML model hosting
  • Deep learning training
  • CUDA applications
  • LLM inference services
  • GPU rendering workloads
  • Data processing pipelines
  • API deployment
  • Background workers
  • Python GPU workloads
  • Node.js GPU services
  • Containerized compute applications

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Creating a New GPU Service

Antryk allows users to deploy GPU-powered applications directly from connected Git repositories using a guided deployment workflow. The deployment system simplifies infrastructure provisioning and GPU workload deployment.

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Create GPU Service Form

The Create GPU Service form allows users to configure repository settings, build pipelines, runtime environments, environment variables, and GPU hardware selection. The deployment workflow includes:
  • Basic service information
  • Git provider connection
  • Repository selection
  • Branch configuration
  • Build configuration
  • Runtime settings
  • Environment variable management
  • GPU infrastructure selection
  • Deployment execution
Create GPU Service Form Part One Create GPU Service Form Part Two Create GPU Service Form Part Three

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Step 1 — Service Information

The Service Information section defines the deployment identity.

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Service Name

Enter a descriptive name for the GPU service. Examples:
  • ai-inference-service
  • production-llm
  • image-generation-worker
  • ml-training-cluster
  • gpu-render-service
Using descriptive service names helps teams identify workloads quickly across environments.

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Step 2 — Source Code Repository

The Source Code Repository section connects the deployment to a Git provider.

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Connect Git Provider

Users can connect supported Git providers for deployment integration. Supported providers include:
ProviderStatus
GitHubAvailable
GitLabComing Soon
BitbucketComing Soon
After connecting the provider, users can select repositories directly from their account.

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Repository Selection

Choose the repository that contains the application source code. Examples: 
nextjs-ssr-app
ml-inference-service
gpu-worker-node
python-training-service

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Branch Selection

Select the Git branch to deploy. Examples: 
main
production
staging
develop
This enables deployment automation directly from source control workflows.

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Step 3 — Build Configuration

The Build Configuration section defines how the application should be installed, built, and started inside the GPU runtime environment. Users can configure:
  • Install command
  • Build command
  • Start command
  • Output directory
  • Root directory
Build Configuration

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Install Command

Defines the dependency installation process. Examples: 
npm install

pip install -r requirements.txt

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Build Command

Defines the application build process. Examples: 
npm run build

python train.py

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Start Command

Defines the runtime execution command. Examples: 
npm start

python app.py

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Output Directory

Defines the generated build output directory. Examples: 
.next
dist
build

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Root Directory

Defines the application root path inside the repository. Examples: 
/
apps/api
services/gpu-worker
The build configuration system provides flexible deployment support across multiple frameworks and runtimes.

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Step 4 — Environment Variables

The Environment Variables section allows users to securely configure runtime secrets and application configuration values. Users can:
  • Add environment variables
  • Import variables
  • Copy existing variables
  • Remove variables
  • Manage secret configurations securely
Examples: 
DATABASE_URL=postgres://localhost:5432/app
OPENAI_API_KEY=xxxx
REDIS_URL=redis://localhost:6379
NODE_ENV=production
This allows applications to securely access infrastructure dependencies and external services.

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Step 5 — Select GPU Plan

The Select Plan section allows users to choose GPU hardware based on workload requirements. Antryk provides multiple GPU infrastructure options optimized for AI, rendering, inference, and high-performance compute workloads. Available GPU plans include:
GPU PlanGPU MemoryRecommended Use Cases
A400016 GBLightweight AI inference and rendering
A450016 GBMid-range GPU compute workloads
RTX 400016 GBVisualization and AI processing
RTX 200016 GBEntry-level GPU compute
L424 GBAI inference and video workloads
A500024 GBDeep learning and rendering
RTX 309024 GBHigh-performance AI training
RTX 4090 PRO24 GBAdvanced inference and compute
A600048 GBLarge-scale model training
A4048 GBEnterprise GPU workloads
L4048 GBAI inference and rendering
L40s48 GBOptimized generative AI workloads
RTX 6000 Ada48 GBProfessional GPU acceleration
A10080 GBEnterprise AI training
H100 Pro80 GBAdvanced large-scale AI workloads
H200 Pro141 GBExtreme high-memory AI compute
The GPU plan selection system enables organizations to optimize infrastructure costs and workload performance. GPU Plan Selection

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Step 6 — Deploy Service

After completing the configuration process, users can deploy the GPU service directly from the dashboard.

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Deploy Workflow

The deployment system automatically:
  • Provisions GPU infrastructure
  • Pulls repository source code
  • Installs dependencies
  • Builds the application
  • Configures runtime services
  • Injects environment variables
  • Starts the application
  • Launches the deployment
Users can deploy the service using the Deploy Service button.

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Supported Workloads

Antryk GPU Services are optimized for:
  • Large Language Models (LLMs)
  • AI inference APIs
  • Stable Diffusion workloads
  • CUDA applications
  • Machine learning training
  • Deep learning pipelines
  • Video rendering
  • Image generation
  • Scientific computing
  • Data processing systems
  • Backend GPU workers
  • AI-powered SaaS applications

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Infrastructure Scalability

Antryk allows organizations to scale GPU workloads based on application demand and infrastructure requirements. Teams can:
  • Upgrade GPU plans
  • Redeploy workloads
  • Modify runtime settings
  • Scale AI infrastructure
  • Optimize compute resources
  • Manage production GPU services centrally
This enables organizations to build scalable AI and compute systems efficiently using managed GPU infrastructure.