GPU Optimized Virtual Machines

We are enabling GPU support in our virtual machines, providing significant advantages for applications that require high performance. The GPUs we offer to optimized for various use cases. Below are detailed descriptions of the cards we provide, including their features and ideal use cases.

NVIDIA L40S

Use Cases: It is ideal for deep learning, artificial intelligence, machine learning, data analytics, and graphics rendering.

Features: The L40S is one of NVIDIA's most powerful GPUs. With high computational power and ample memory support, it is perfect for AI model training and data analysis. This card, capable of handling numerous parallel tasks, delivers top-tier performance for users working with large datasets.

Recommended Use: Best suited for big data processing, video analytics, simulations, and AI-based applications.

When to Choose the L40S: If you’re working with cutting-edge AI models, need to process massive datasets quickly, or require top-tier performance for simulation-heavy workloads, the L40S is the optimal choice.

NVIDIA T4

Use Cases: Designed for artificial intelligence, machine learning, video processing, and cloud-based applications.

Features: The NVIDIA T4 is a versatile GPU suitable for use in data centers and cloud environments. It strikes a balance between processing power and energy efficiency. The T4 excels in video encoding, AI inference, and deep learning model optimization.

Recommended Use: Ideal for AI inference, video streaming processing, and cloud gaming servers.

When to Choose the T4: If your workloads require a balanced combination of performance and energy efficiency, such as real-time AI inference, video streaming, or medium-scale machine learning tasks, the T4 offers the right mix of value and performance.

NVIDIA A30

Use Cases: Designed for data science, AI training, and high-performance computing (HPC) applications.

Features: The A30 is an entry-level model in the A100 series, offering high efficiency for data analytics and machine learning tasks. It provides balanced performance for both training and inference operations. Additionally, its ability to handle multiple parallel tasks makes it suitable for large-scale AI projects.

Recommended Use: Effective for deep learning models, data analytics, simulations, and AI training.

When to Choose the A30: Choose the A30 if your workloads demand solid performance for data analysis, AI model training, or simulations but don’t require the highest-end computational power. It’s ideal for users who need good performance at a more moderate price point.

How to Choose the Right GPU for Your Needs

While all three GPUs L40S, T4, and A30 offer high performance, the right card for you depends on your specific workload. Here are some factors to consider:

Workload Type:

For AI Model Training & Large-Scale Data Processing: Choose the L40S for top-tier performance and memory capacity.
For AI Inference & Video Processing: Choose the T4 for a cost-effective, versatile solution with good performance for real-time applications.
For Data Science & Mid-Range AI Workloads: The A30 is ideal if you need a balance of power and efficiency for machine learning and data science applications.

Performance Needs:

If your tasks require cutting-edge performance and handle massive datasets or complex simulations, the L40S is the best fit.
If you're looking for a more energy-efficient and cost-effective solution for AI inference, T4 is your best option.
For most data analytics and AI model training projects that don’t need the extreme power of the L40S, the A30 provides a solid balance.

Budget Considerations:

L40S is the highest-end option and comes at a premium price point. It’s worth the investment if your workloads justify the need for top-tier performance.
T4 offers excellent value for smaller AI inference and video processing tasks, making it a great option for budget-conscious projects.
A30 provides a budget-friendly option by offering strong performance at a reasonable price point.

GPU Optimized Virtual Machines