AMD vs NVIDIA: Which is the Best GPU for a Server?

AMD and NVIDIA are the leading tech providers in the Graphics Processing Unit market. In the context of GPU servers, they both strive to offer high-performance chipsets for large and extreme computational tasks such as Artificial Intelligence workloads, Machine Learning, scientific computing, and High-Performance Computing.

Although their main goal in the context of GPU servers is to provide efficient solutions in handling parallel computational tasks, there are many key differences between the two graphic cards that one should consider before choosing.

This article informs you about the differences between AMD and NVIDIA and the use cases for each graphics card so you can make a wise decision in selecting the right GPU for your server.

#Key differences between AMD and NVIDIA

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Although AMD and NVIDIA compete for market share through a top focus on high-performance computing and AI professional environments there are many key differences between the two.

#Architecture

A highly notable key difference between the two is the GPU architecture. The CUDA architecture has given NVIDIA several advantages including performance gains in data-intensive applications, optimized libraries for deep learning, adaptability in diverse markets of High Performance Computing, and more importantly, a rich and friendly environment for developers.

On the other hand, AMD’s GPUs are built on RDNA and CDNA architectures. While CUDA has helped NVIDIA significantly in penetrating the AI market and becoming the go-to choice for Machine Learning projects, AMD brings strong competition with its MI100 and MI200 series. Designed for intensive AI workloads and HPC, AMD’s MI100 and MI200 aim to challenge NVIDIA’s A100 and H100.

#Software ecosystem

With CUDA providing an immense ecosystem of tools, NVIDIA has proven to be mature in supporting developers of different backgrounds in research and commercial industrial projects related to AI/ML. Due to the out-of-the-box optimization of top machine learning frameworks such as PyTorch to work with CUDA, NVIDIA has a clear advantage in dominating the AI/ML market.

Open-source libraries and frameworks offered by AMD are a great alternative for those who don’t want to develop their projects using NVIDIA’s proprietary software. AMD’s ROCm platform is a compelling option for creating and maintaining ecosystems for data analytics and high-performance computing projects in which requirements are less than in deep learning. Although AMD lags in driver support and its software ecosystem is not as mature as NVIDIA’s, it is continuously improving with each new release.

#Performance

NVIDIA’s specialized hardware for AI workloads is crucial in increasing performance in deep learning tasks. Optimized for mixed-precision operations, Tensor Cores in NVIDIA GPUs speed up AI and deep learning operations. As an example, it’s worth mentioning the performance metrics of up to 312 teraFLOPS operations in TF32 mode in A100.

Although AMD has no direct equivalent to NVIDIA’s Tensor Cores, its MI series makes use of Matrix Cores technology for accelerating AI workloads. CDNA1 and CDNA2 architectures make AMD competitive with NVIDIA in deep learning projects. More specifically, the MI250X chips offer performance similar to NVIDIA’s Tensor Cores.

#Costs

NVIDIA’s GPU-specialized hardware and its top-notch software stack, especially in AI and ML, justify the higher price points when compared to those offered by AMD. Tensor Cores and CUDA make the difference in processing heavy AI workloads in the most efficient possible way, cutting the project’s investments over time.

AMD price points for its GPUs are way more affordable than those of NVIDIAs with the disadvantage of low performance in intensive AI workloads compared to Ampere and H100. In the case of general high-performance computing projects or small AI/ML tasks, AMD GPUs are worth the investment.

#Cloud integration

Having a larger footprint in the cloud when compared to AMD, NVIDIA is the first choice for developers who want to enhance GPU acceleration for their AI and ML projects. Shipping a software suite with preconfigured AI models, deep learning libraries and frameworks like PyTorch and TensorFlow, Nvidia’s NGC sets a difference when it comes to providing an AI/ML ecosystem in the cloud.

The integration of NVIDIA’s GPU by top cloud providers such as Cherry Servers, Google Cloud, and AWS doesn’t mean that AMD is far behind in any way. With its MI series, AMD has made undeniable progress in the cloud space by partnering with Microsoft Azure. By emphasizing open-source solutions with its ROCm platform, AMD is potentially growing among open-source developers running their projects in the cloud.

#What are the similarities between AMD and NVIDIA?

AMD and NVIDIA offer scalable solutions to support modern intensive workloads in AI, HPC, and cloud computing. Despite their architectural and software ecosystem differences, there are many similar aspects to both companies.

#Performance per watt and energy efficiency

AMD and NVIDIA have a high focus on performance improvement for each unit of power consumed by their respective GPUs. While NVIDIA’s Ampere A100 and Hopper H100 series offer highly optimized architectures that deliver strong performance gains and reduce power consumption at the same time, AMD brings significant improvements in performance per watt with its MI250X.

Performance is key in HPC, but energy efficiency is crucial. Both companies offer specialized solutions to minimize energy loss and reduce the costs of the electric bill, especially in large-scale GPU data servers. For instance, RDNA 3 architecture in AMD utilizes 6nm processes to deliver better performance at lower power consumption compared to previous generations.

#Cloud support and integration

Cloud service providers attract many organizations and developers who are interested in deep learning, scientific computing, and HPC. Considering this, both AMD and NVIDIA have made huge progress in the cloud computing space through partnerships with major cloud providers.

Both companies provide cloud-based GPUs for intensive computational tasks. Used in accelerating AI-related tasks in the cloud, these GPUs are also paired with specialized software to optimize workloads.

#High-Performance computing

Intensive computation tasks in high-performance computing require graphic processing units that are designed to process millions of threads in parallel. AMD and NVIDIA provide both the parallel processing capabilities and the bandwidth that are required to process large datasets in HPC projects.

With their GPUs featuring thousands of cores, both AMD and NVIDIA compete to handle computation-heavy tasks efficiently. Offering a robust ecosystem for developers to take maximum advantage of the power of their GPUs, both companies hold the top position in integrating into high-performance servers, supercomputing systems, and major cloud providers.

#Software Ecosystem

The full potential of hardware can’t be leveraged without the software specifically designed for it. AMD and NVIDIA are fully invested in developing libraries that can be utilized to take full advantage of their GPUs.

NVIDIA’s CUDA and cuDNN provide developers with a set of tools for developing and deploying AI/ML applications. AMD provides machine-learning capabilities through its open-source software platform ROCm.

Continually evolving their AI offerings, AMD and NVIDIA support major AI frameworks such as:

• TensorFlow
• PyTorch

This extensive support for AI/ML frameworks optimized for their GPUs allows AMD and NVIDIA to make progress in targeting high-demand markets in industries that deal with heavy AI workloads, such as healthcare, automotive, and finance.

Also check out: Best GPUs for Mining

#When to use NVIDIA over AMD

#AI/ML workloads

NVIDIA’s rich set of libraries and tools oriented towards AI and deep learning along with its Tensor Cores in the newer GPU architectures make NVIDIA the preferable choice in AI/ML workload tasks. Particularly the A100 and H100 substantially accelerate training in deep learning operations, offering a performance that isn’t yet matched by its AMD counterparts.

CUDA’s deep integration with the top machine learning frameworks is another important factor that hugely contributes to NVIDIA’s dominance of the AI/ML market.

#Cloud providers

NVIDIA’s unique hardware innovations combined with integration by huge providers such as Google Cloud, AWS, Microsoft Azure, and Cherry Servers are key to its dominance in the cloud. Characterized by its unmatched GPU performance and strong software stack, NVIDIA has managed to position itself as the top key player in cloud solutions for AI/ML projects.

Choosing among optimized GPU instances powered by NVIDIA, you can train and deploy AI/ML models at scale in the cloud.

Also check out: GPU temperature guide

#When to use AMD over NVIDIA

#Budget-conscious deployments

Offering more cost-effective GPUs than their NVIDIA counterparts, AMD tends to be the primary choice for budget-conscious deployments. Due to its superior raw computation performance per dollar, AMD is widely used in large-scale environments where cutting the electric bill is crucial.

#High-Performance Computing

AMD’s Instinct MI series are well optimized for certain workloads in scientific computing, making AMD competitive with NVIDIA in HPC. Offering strong double precision performance AMD’s MI100 and MI200 are perfect for large-scale scientific tasks at a lower cost compared to AMD’s counterparts.

#Open-source ecosystem

AMD’s focus on open-source software and libraries might be a better option than NVIDIA if you are building a highly customizable solution that requires flexibility. NVIDIA’s proprietary ecosystem is not a good fit for open-source users who value freedom.

Also check out: Best GPUs for Gaming

#Conclusion

Choosing between AMD and NVIDIA depends on your specific workload, budget, and the software ecosystem you want to work with. In summary, we can conclude that NVIDIA is the best choice for AI/ML projects, while AMD is the best fit for cost-effective GPU options in scientific computing and large-scale data centers.

Cherry Servers offers both AMD and NVIDIA GPUs for dedicated servers perfectly suited for high-end workloads. Deploy your GPU-dedicated server in only 15 minutes with automatic preparation and provision from the ground up at reasonable prices.