What are the best GPU servers?

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

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

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

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

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