A lot has changed in the world of high-performance graphics processors in recent years. Given the increasing importance of GPU servers for computing-intensive applications, it’s essential to choose the right hardware for your use case. Below we offer a comparison of some of the best GPU servers.

GPU server comparison

NVIDIA H100

The NVIDIA H100 is currently NVIDIA’s most powerful GPU model and is targeted towards organizations that require top performance. The Tensor Core GPU is based on Hopper architecture that was specially developed for the requirements of modern applications in areas like artificial intelligence, high-performance computing and data-heavy applications. With its support for memory technology like HBM3 and innovative features like the FP8 data type, the H100 takes efficiency and speed to the next level.

Thanks to integrated fourth-generation NVLink technology, several GPUs can be connected in a powerful cluster, which can increase computing power even more. The GPU was developed for very large neural networks and data-heavy tasks such as those involved in language models like GPT and scientific simulations.

Technical specifications

  • Manufacturing technology: 4 nm (TSMC)
  • Computing power: Up to 60 TFLOPS (FP64) and over 1000 TFLOPS (Tensor Cores)
  • Memory: HBM3 with up to 80 GB
  • NVLink: Enables connection with several GPUs with high bandwidth
  • Special features: Supports FP8 data type for efficient training of larger AI models

Advantages and disadvantages

Advantages Disadvantages
Excellent performance for AI training and inference Very high price
Supports the latest memory technology High energy use (TDP up to 700 watts)
Scalability with NVLink

NVIDIA A30

The NVIDIA A30 is a versatile GPU that is geared towards companies looking for a robust yet cost-effective solution. It’s based on Ampere architecture, which is known for its balance between performance and efficiency. The A30 combines solid performance with relatively low energy consumption, which makes it ideal for use in AI inference, moderate HPC applications and virtualization.

Technical specifications

  • Manufacturing technology: 7 nm (TSMC)
  • Computing power: Up to 10 TFLOPS (FP64), 165 TFLOPS (Tensor Cores)
  • Memory: 24 GB HBM2
  • NVLink: Up to two GPUs can be connected

Advantages and disadvantages

Advantages Disadvantages
Good value for money Not suited to very large models
Lower energy use (TDP of 165 watts) Limited memory compared to H100
ECC support for memory integrity

Intel Gaudi 2

The Intel Gaudi 2 is a 24-core processor specially designed for AI training and is a viable alternative to NVIDIA GPUs. It was developed by Habana Labs, a subsidiary of Intel, and is designed to be particularly efficient and powerful for typical AI workloads like transformer models and machine learning.

The focus of the Gaudi 2 is on optimizing training workloads, primarily for large neural networks that require high computing and memory bandwidth. Its open software ecosystem and the integration of RDMA (Remote Direct Memory Access) offer advantages in terms of scalability in multi-GPU environments.

Technical specifications

  • Manufacturing technology: 7 nm
  • Memory: 96 GB HBM2e
  • Special features: RDMA and RoCE support for direct memory access between GPUs

Advantages and disadvantages

Advantages Disadvantages
Optimized for AI training (especially transformer models) Less versatility for general HPC applications
High memory throughput Less software support compared with NVIDIA
Lower licensing costs due to open software ecosystems

Intel Gaudi 3

The Intel Gaudi 3 is an AI-specific graphics processor and builds on the Gaudi 2. With its improved computing power and memory technology, it’s designed to further optimize the efficiency and scalability of AI models.

It offers higher performance for AI training tasks, especially applications in the area of generative AI such as large language models and image processing. The interconnect technology was also improved, which makes it a great choice for cluster solutions.

Technical specifications

  • Manufacturing technology: 5 nm
  • Computing power: Up to 1,835 PFLOPS (FP8)
  • Memory: Up to 120 GB HBM2e
  • Special features: Advanced interconnect infrastructure

Advantages and disadvantages

Advantages Disadvantages
Higher performance for AI applications Like Gaudi 2, limited applications outside AI
Improved interconnect for cluster solutions Relatively new on the market, meaning less testing
More energy efficient than Gaudi 2

How to choose the right GPU server for your use case

Which GPU server is right for your company will depend on what you intend to use it for. Before investing in one, be sure to analyze your workload and the long-term requirements of your applications.

AI training and deep learning

Memory bandwidth, computer power and scalability are crucial when training large neural networks and transformer models like GPT. Both the NVIDIA H100 and the Intel Gaudi 3 are suitable in this respect. The Intel Gaudi 2 could be an interesting alternative for budget-conscious projects, especially for specific workloads.

Recommendation:

  • High end: Intel Gaudi 3
  • Budget solution: Intel Gaudi 2

AI inference

When it comes to inference, that is the use of trained models, efficiency and energy use are the most important considerations. The NVIDIA A30 is the ideal choice for many applications, as it offers sufficient performance with low energy use.

Recommendation:

  • NVIDIA A30

High-performance computing

For scientific calculations and simulations that frequently require FP64 performance, the NVIDIA H100 is second to none. The NVIDIA A30 could also be an option for smaller simulations or less demanding workloads.

Recommendation:

  • High end: NVIDIA H100
  • Budget solution: NVIDIA A30

Big data and analytics

High memory throughput is crucial for data-heavy applications like real-time analysis. Both the NVIDIA H100 GPU and the Intel Gaudi 3 are good choices here, though the Gaudi 3 scores extra points with its lower price.

Recommendation:

  • NVIDIA H100
  • Intel Gaudi 3

Edge computing and smaller clusters

For applications like edge computing that require lower energy use, the NVIDIA A30 is a good choice thanks to its lower power use and good performance.

Recommendation:

  • NVIDIA A30
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