Run AI inference, model training, and GPU-accelerated workloads on dedicated GPU instances — pay only for the compute hours you use.
Contact our team for tailored enterprise solutions that match your exact needs.
Talk to SalesMost GPU cloud providers price hardware on opaque per-hour compute-unit formulas that are hard to map to your actual workload. On Antryk, three things determine your bill:
Your rate is set by the GPU hardware you select — from 16 GB cards like the A4000 for lightweight inference up to 141 GB on the H200 Pro for extreme-memory AI workloads. The rate is fixed per plan, not a variable compute-unit calculation.
Choose hardware sized to your actual workload — entry-level cards for rendering and light inference, mid-range GPUs for deep learning and generative AI, and enterprise-class cards for large-scale training and high-memory LLM inference.
Git integration, build configuration, environment variable management, and infrastructure provisioning are part of every plan. You pay for the GPU tier running your workload — not for the pipeline that deploys it.
Connect your repo, configure the build, set your secrets, and pick a GPU plan. Antryk provisions infrastructure, installs dependencies, builds your app, and launches it on dedicated GPU hardware.
Link GitHub (GitLab and Bitbucket coming soon), pick the repository and branch to deploy — main, production, staging, or develop — and Antryk handles the rest.
Set your install command (pip install -r requirements.txt, npm install), build command (python train.py, npm run build), and start command — plus root and output directories for monorepos.
Add environment variables like DATABASE_URL, OPENAI_API_KEY, or REDIS_URL before deployment. Import, copy, or remove variables without ever hardcoding secrets into your repo.
Choose from 16 GB entry cards to 141 GB enterprise GPUs based on your workload, then hit Deploy Service — infrastructure provisioning, dependency installs, and runtime startup happen automatically.
Every Antryk GPU deployment automatically provisions infrastructure, pulls your repository, installs dependencies, builds your app, and starts the runtime — with no separate orchestration charge.
Scale as your model grows: Start on a smaller card while you validate your workload, then upgrade your GPU plan and redeploy as training size or inference traffic increases — without rebuilding your deployment configuration from scratch.
Competitors charge separately for orchestration, build pipelines, and secret management. On Antryk, all of this is bundled into every GPU service plan.
Git-Based Deployments
Connect GitHub, select a branch, push to deploy
Custom Build & Start Commands
Full control over install, build, and runtime execution
Secure Environment Variables
Add, import & manage secrets before deployment
Flexible Root & Output Directories
Deploy monorepos and nested service structures
Free GPU Plan Upgrades
Move to a larger card anytime, no redeploy from scratch
Containerized Workload Support
Deploy Python, Node.js, or containerized GPU apps
One-Click Infrastructure Provisioning
Dependencies, build, and runtime configured automatically
GPU memory requirements vary enormously by workload. Here's how to think about it for common AI and compute-intensive use cases.
The deciding factor is whether your workload needs GPU acceleration, not just runtime compute. Here's the framework:
Everything you need to know about Antryk GPU Service pricing, hardware tiers, and deployment.
Every service runs on the same platform — one dashboard, one bill, zero context-switching.
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