In the world of high performance GPUs, things have changed a lot in recent years. With the growing importance of GPU servers for intensive calculation applications, it is essential to choose the equipment adapted to your needs.
GPU servers
Hardware dedicated with a powerful graphics card
Use the GPU calculation power in all flexibility to manage large amounts of data and only pay the resources used.
Comparison of GPU performance for servers
NVIDIA H100
The NVIDIA H100 is currently the most powerful model of the NVIDIA GPU portfolio and is aimed at organizations that need advanced performance. GPU Tensor's heart is based on Hopper architecture, specially designed to meet the requirements of modern applications in the fields of artificial intelligence, of the High Performance Computing (HPC) and high data intensity applications. With its management of the latest memory technologies such as HBM3 and its innovative functions such as the FP8 data type, the H100 brings efficiency and speed to a higher level.
Thanks to the fourth generation NVLink integrated NVLink technology, several GPUs can be connected in a powerful cluster, in order to significantly increase calculation power. This GPU was designed For very large neural networks and gourmet data taskslike those required in language models such as GPT and scientific simulations.
Technical details
- Manufacturing technology : 4 nm (TSMC)
- Power of calculation : up to 60 tflops (FP64) and more than 1000 tflops (Tensor Cores)
- Memory : HBM3 with up to 80 GB
- Nvlink : allows the connection of several GPUs with a high bandwidth
- Particularities : supports the FP8 data type for more efficient training of large AI models
Advantages and disadvantages
| Benefits | Disadvantages |
|---|---|
| Excellent performance for AI training and inferences | Very high purchase price |
| Supports the most modern memory technologies | High energy consumption (TDP up to 700 watts) |
| Scalability thanks to Nvlink |
NVIDIA A30
The NVIDIA A30 is a versatile GPU specially designed for companies looking for a solution that is both robust and profitable. It is based on Ampere architecture, known for its balance between performance and efficiency. The A30 combines a solid computing power with relatively low energy consumption, which makes it ideal for a Use in AI inference, moderate HPC applications and virtualization.
Technical details
- Manufacturing technology : 7 nm (TSMC)
- Power of calculation : up to 10 tflops (FP64), 165 tflops (Tensor Cores)
- Memory : 24 GB HBM2
- Nvlink : up to two GPUs can be connected
Advantages and disadvantages
| Benefits | Disadvantages |
|---|---|
| Good value for money | Not suitable for very large models |
| Lower energy consumption (165 Watts TDP) | Limited memory bandwidth compared to H100 |
| ECC management for the integrity of memory |
Intel Gaudi 2
Specially designed for IA training, the Intel Gaudi 2 is a 24 Tensor core processor which constitutes a serious alternative to the NVIDIA GPU. Developed by Habana Labs, a subsidiary of Intel, the Gaudi 2 was designed to be particularly effective and powerful for Typical workloads of artificial intelligencelike models of transformers and Machine learning.
GAUDI 2 focuses on optimizing learning workloads, in particular for large neural networks that require a large bandwidth and memory bandwidth. Its open software ecosystem and the integration of the RDMA (Remote Direct Memory Access) offer advantages in terms of scalability in multi-GPU environments.
Technical details
- Manufacturing technology : 7 nm
- Memory : 96 GB HBM2E
- Particularities : RDMA and ROCE support for direct access to memory between GPUs
Advantages and disadvantages
| Benefits | Disadvantages |
|---|---|
| Optimized for learning AI (in particular models of transformers) | Less versatility for general HPC applications |
| High memory flow | Reduced software support compared to Nvidia |
| Lower license costs thanks to the open software ecosystem |
Intel Gaudi 3
The Intel Gaudi 3 is the next GPU specific to Intel AI and represents an evolution of the GUIDI 2. GPU. With an improved calculation power and memory technology, the GAUDI 3 is mainly designed for Optimize the efficiency and scalability of AI models more.
This GPU offers even higher performance for AI learning tasks, in particular for applications in the field of generative AI, of Large Language Models and for image processing. Interconnection technology has also been improved, making it an excellent choice for large cluster solutions.
Technical details
- Manufacturing technology : 5 nm
- Power of calculation : up to 1.835 pflops (FP8)
- Memory : up to 120 GB HBM2E
- Particularities : advanced interconnection infrastructure
Advantages and disadvantages
| Benefits | Disadvantages |
|---|---|
| Even higher performance for AI applications than Gaudi 2 | … But, like Gaudi 2, its use remains mainly limited to AI |
| Improved interconnection for cluster solutions | Relatively new on the market, so few practical cases tested |
| More efficient on the energy level than Gaudi 2 |
Note
Do you want to use the performance of server GPUs for your projects? With Ionos GPU servers, this is no problem. 100 % servers of servers comply with GDPR are suitable for various areas of application and are billed per minute.
User scenarios and recommendations
The server GPU suitable for your business depends entirely on your individual use case. Before investing, it is therefore important to analyze your workloads and assess the long -term needs of your applications.
AI and Deep Learning training
For the training of large neural networks and in particular Transformer models such as GPT, the bandwidth of memory, calculation power and scalability are decisive. The Nvidia H100 is right here as much as the CPU Intel Gaudi 3, which according to some benchmarks results until 1.7 times faster during LLM training. However, for tighter budgets, Intel Gaudi 2 can be an interesting alternative, especially for specific workloads.
Recommendation ::
- High end: Intel Gaudi 3
- Economy solution: Intel Gaudi 2
IA inference
For inference, that is to say the use of trained models, it is above all the efficiency and energy consumption that are important. The NVIDIA A30 is an ideal choice here for many applications, as it offers sufficient performance with reduced energy consumption.
Recommendation ::
High Performance Computing
For scientific calculations and simulations which often depend on the FP64 performance, the Nvidia H100 is unequaled. The NVIDIA A30 can also be an option for small simulations or less demanding workloads.
Recommendation ::
- High end: NVIDIA H100
- Economic solution: NVIDIA A30
Big Data and Analytics
For gourmet data applications, such as real -time analyzes, a high memory flow is essential. In this area, the NVIDIA H100 GPU and the Intel Gaudi 3 are both convincing, even if the Gaudi 3 stands out thanks to its lower price.
Recommendation ::
- NVIDIA H100
- Intel Gaudi 3
Edge Computing and Small Clusters
For applications such as Edge Computing, which require lower energy consumption, Nvidia A30 is an appropriate choice thanks to its reduced energy consumption and good performance.
Recommendation ::
