What factors matter when picking an Enterprise GPU Server?


Picking a GPU server can seem daunting and quite an expensive experience. Here are a few things to think about. To choose the best Enterprise GPU server for your needs, consider the following factors:

  • Workload: What type of workload will you be running on the GPU? Different workloads have different requirements in terms of compute, memory, and networking capabilities.

  • Performance: How much performance do you need? The performance of a GPU is measured in terms of FLOPS (floating-point operations per second).

  • Memory: How much memory do you need? The memory capacity of a GPU is measured in gigabytes (GB).

  • Networking: What type of networking connectivity do you need? GPUs can be equipped with different types of networking ports, such as Ethernet, InfiniBand, and NVLink.

  • Form factor: What form factor do you need? GPUs come in a variety of form factors, including rack-mounted, blade, and tower.

Once you have considered these factors, you can use the following table to compare the different NVIDIA Enterprise GPUs we see the most used:

FeatureA100H100
ArchitectureAmpereHopper
Cores691214592
Base Clock1410 MHz1530 MHz
Boost Clock1620 MHz1845 MHz
TDP250 W350 W
Memory40 GB HBM2e80 GB HBM3
Memory Bandwidth1.6 TB/s3.2 TB/s
FP32 Performance31.2 TFLOPS63.1 TFLOPS
FP64 Performance15.6 TFLOPS31.5 TFLOPS


Key Differences

The NVIDIA H100 GPU is the successor to the A100 GPU and offers a number of significant improvements. These include:

  • Increased cores: The H100 GPU has more than twice as many cores as the A100 GPU, which results in significantly higher performance.

  • Faster clocks: The H100 GPU has higher base and boost clocks than the A100 GPU, which also contributes to its higher performance.

  • More memory: The H100 GPU has twice as much memory as the A100 GPU, which allows it to handle larger datasets and models.

  • Higher memory bandwidth: The H100 GPU has twice the memory bandwidth of the A100 GPU, which enables it to transfer data more quickly between the GPU and the CPU.

Conclusion

The NVIDIA H100 GPU is a major upgrade over the A100 GPU and offers significant improvements in performance, memory, and bandwidth. This makes it an ideal choice for high-performance computing applications such as artificial intelligence, machine learning, and graphics rendering.

As a premier integrator of GPU servers you can talk to us.


General Enquiry

What factors matter when picking an Enterprise GPU Server?


Picking a GPU server can seem daunting and quite an expensive experience. Here are a few things to think about. To choose the best Enterprise GPU server for your needs, consider the following factors:

  • Workload: What type of workload will you be running on the GPU? Different workloads have different requirements in terms of compute, memory, and networking capabilities.

  • Performance: How much performance do you need? The performance of a GPU is measured in terms of FLOPS (floating-point operations per second).

  • Memory: How much memory do you need? The memory capacity of a GPU is measured in gigabytes (GB).

  • Networking: What type of networking connectivity do you need? GPUs can be equipped with different types of networking ports, such as Ethernet, InfiniBand, and NVLink.

  • Form factor: What form factor do you need? GPUs come in a variety of form factors, including rack-mounted, blade, and tower.

Once you have considered these factors, you can use the following table to compare the different NVIDIA Enterprise GPUs we see the most used:

FeatureA100H100
ArchitectureAmpereHopper
Cores691214592
Base Clock1410 MHz1530 MHz
Boost Clock1620 MHz1845 MHz
TDP250 W350 W
Memory40 GB HBM2e80 GB HBM3
Memory Bandwidth1.6 TB/s3.2 TB/s
FP32 Performance31.2 TFLOPS63.1 TFLOPS
FP64 Performance15.6 TFLOPS31.5 TFLOPS


Key Differences

The NVIDIA H100 GPU is the successor to the A100 GPU and offers a number of significant improvements. These include:

  • Increased cores: The H100 GPU has more than twice as many cores as the A100 GPU, which results in significantly higher performance.

  • Faster clocks: The H100 GPU has higher base and boost clocks than the A100 GPU, which also contributes to its higher performance.

  • More memory: The H100 GPU has twice as much memory as the A100 GPU, which allows it to handle larger datasets and models.

  • Higher memory bandwidth: The H100 GPU has twice the memory bandwidth of the A100 GPU, which enables it to transfer data more quickly between the GPU and the CPU.

Conclusion

The NVIDIA H100 GPU is a major upgrade over the A100 GPU and offers significant improvements in performance, memory, and bandwidth. This makes it an ideal choice for high-performance computing applications such as artificial intelligence, machine learning, and graphics rendering.

As a premier integrator of GPU servers you can talk to us.


General Enquiry