NVIDIA A30 Vs T4 Vs V100 Vs A100 Vs RTX 8000 GPU cards

November 9, 2021
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Graphics Processing Unit, also known as the graphics card, helps produce graphics and ray tracing. GPU is an expansion card responsible for creating interactive graphics in mobile devices, laptops, and PCs.

NVIDIA provides a wide variety of GPUs. Based on the performance of different types of NVIDIA graphics cards, we will recommend a comparative based study on different types of graphics cards. First, we will look at some basic features about all kinds of graphics cards like NVIDIA A30, T4, V100, A100, and RTX 8000 given below.

NVIDIA A30 – NVIDIA A30 helps to perform high-performance computing systems. A30 incorporates fast memory bandwidth. Also, Low power consumption is proven to be beneficial for mainstream computations. It is an end to end and hypervisor-based infrastructure,

NVIDIA T4 - NVIDIA T4 focuses explicitly on deep learning, machine learning, and data analytics. With the ability to perform a high-speed computational system, it offers various features. This advanced GPU model is quite energy-efficient.

NVIDIA V100 – NVIDIA V100 offers advanced features in the world of data science and AI. It comes with the facility of optimum memory usage. The 32 GB models of NVIDIA V100 graphics card can compile the tasks of 100 computers into one computer at a time. As a result, it becomes pretty efficient.

NVIDIA A100 – NVIDIA A100 is an evolutionary step that enables more high-speed computation than the previous models. It comprises a single AI infrastructure that contains all the interference and analytics together. Comparing this graphics card with the previous ones provides computational speed up to 20 times. It comes with both 40 GB and 80GB models. The 80 GB model provides the fastest bandwidth in the world compared to other models (2 TB/s).

RTX 8000 – RTX 8000 merges high-speed memory capacity with performance to build an AI-enhanced system. By creating the most critical model, it is designed to create workloads inclined to data science. It helps the professional applications to perform different functions and connect with the data science fields.

Now, we will look at the table given below to look for a comparative study –

    GPUNVIDIA A30NVIDIA T4NVIDIA V100NVIDIA A100RTX 8000  GPU Architecture  NVIDIA Ampere    NVIDIA Turing  NVIDIA Turing and Volta    NVIDIA Ampere    NVIDIA Turing  Memory Size    24 GB       16 GB   32 GB    40 GB    48 GB  CUDA cores      3804  2560  5120  6912  4608    Bandwidth    933 GB/s    320 GB/s    900 GB/s    1.6 TB/s    672 GB/s    TDP    165 W    70 W    250 W    400 W    295 W  Precision Performance  10.3 Tflops  8.1 Tflops  15.7 Tflops  19.5 Tflops  16.3 Tflops  30 days committed price     INR 40,000    INR   17,500    INR 50,000         INR 75,000     INR 44,047      Virtualization Workload  A high-performance computing system, fast memory bandwidth, Low power consumption    High-speed computational system    Optimum memory usage    high-speed computation    high-speed memory capacity and performance helps to gap the bridge between technical applications with data science fields

The table given above will help our readers to get an idea about the architecture, memory size, memory, power, committed price, visualization workload, precision performance, 30 days committed price of the different graphics cards of NVIDIA.

Selecting the appropriate type of GPU

Now, we will provide some information about which GPU to use depending on your workload.

● NVIDIA A100 is the most advanced of all models of GPUs that fits the best in data centers and, it offers a high-speed computational system. It is best to use NVIDIA A100 in the field of data science. NVIDIA A100 has the latest Ampere architecture.

● NVIDIA A30 provides ten times higher speed in comparison to NVIDIA T4.

● Like NVIDIA A100, NVIDIA V100 also helps in the data science fields. But the NVIDIA V100 is not suitable to use in gaming fields.

● RTX 8000 is the best NVIDIA graphics card for gaming.

Here we have already discussed the individual features of various types of graphics cards. From the comparison-based study, our readers will be able to get some quick knowledge at a glance. Different types of uses based on their unique features are also discussed above. Also, what type of NVIDIA graphics card is suitable for what purpose should be clear to them.

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