Model Deployment Overview

The Model Deployment page offers an intuitive interface for deploying machine learning models by selecting the desired GPU configurations. It provides detailed hardware specifications, pricing breakdowns, and resource management features to streamline the deployment of models.

Key Features:

1. Number of GPUs:

   • Users can choose the number of GPUs required for deployment, with options as follows:
     • 1X: Single GPU
     • 2X: Double GPU
     • 4X: Quad GPU
     • 8X: Eight GPUs
   • The selection of GPUs impacts model performance, with more GPUs offering increased processing power and speed, but also higher costs.

2. Price Breakdown:

   • Users can access a comprehensive price breakdown for GPU usage, disk space, and overall deployment costs based on their selected configuration.
   • Example: For the 2X GPUs configuration, the cost breakdown includes:
     • GPU On-Demand Rate (e.g., 0.28 USD/hr)
     • Disk Usage Cost (e.g., 16 GB at 0.00 USD/hr)
     • Total daily and monthly costs, helping users estimate deployment expenses more accurately.
   • Users can directly proceed with the Rent Now button to initiate GPU usage for the deployment.

3. Detailed GPU Specifications:

   • The platform provides detailed specifications for each selected GPU, including:
     • GPU Name: The model of the GPU (e.g., 1x A40).
     • GPU RAM: The available GPU RAM, critical for large models..
     • CPU Information: Number of cores, CPU model, and architecture.
     • Memory Bandwidth: Shows the available memory bandwidth, which affects data transfer speeds.
     • Max CUDA Cores: Specifies the maximum number of CUDA cores for parallel processing.
     • Ports and Disk Space: Information about available ports and disk space for storage.
     • Reliability and Internet Speeds: Highlights the system's reliability percentage and download/upload internet speeds.

4. Plan Selection and Deployment:

   • After reviewing the GPU details, users can click the Plan button to finalize the GPU and deployment settings.
   • When ready, click the Deploy button (located at the top right of the page) to begin the deployment process..
   • The page will also provide real-time feedback and cost estimates based on the selected GPU configuration.

How to Use:

1. Step 1: Choose the desired number of GPUs (1X, 2X, 4X, or 8X).
2. Step 2: Review the detailed price breakdown and GPU specifications.
3. Step 3: Click on the Plan button to confirm the GPU setup.
4. Step 4: Click Deploy to begin the model deployment process.

The Model Deployment page offers a streamlined experience for deploying machine learning models, allowing users to manage both hardware resources and costs effectively.

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