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GPU Instances

The GPU Instances feature empowers users to kickstart their AI and ML projects by selecting the ideal GPU configuration tailored to their performance requirements and budget constraints. Whether you're running lightweight experiments or heavy-duty deep learning models, choosing the right GPU can significantly influence speed and cost-efficiency.

Key Advantages​

  • Optimized Performance: Pick from a range of GPU options (e.g., NVIDIA A100, RTX 3090, etc.) to accelerate training and inference.
  • Cost Efficiency: Balance between computational power and budget to suit projects of varying scales.
  • Flexible Integration: Seamlessly combine GPU instances with AI/ML templates such as TensorFlow, PyTorch, ComfyUI, Langflow, and more.
  • Customizable Environments: Start with a pre-configured template or modify it to perfectly match your workload needs.
  • Scalable Resources: Easily upgrade or switch GPU instances as your project demands evolve.

How It Works​

  1. Select GPU Instance: Begin by selecting the GPU that aligns with your workload’s performance and cost goals.
  2. Choose or Customize Template: Pick an AI/ML template or customize your environment to include necessary packages.
  3. Launch Environment: Deploy your tailored workspace quickly without manual configuration.
  4. Scale as Needed: Adjust GPU resources or switch templates without starting over.

Boost Your Workflow​

By coupling GPU Instance selection with AI/ML Templates, you streamline the deployment pipeline, enabling faster model development, testing, and iteration—all while keeping your resource use efficient and cost-effective.

Ready to select your ideal GPU and get started? Combine the power of GPU Instances with AI/ML Templates to launch your optimized development environment today!