5 Best GPUs for HPC Workloads in 2023–24

Karlos G. Ray [Masters | BS-Cyber-Sec | MIT | LPU]

8 min readAug 17, 2023

In this compute-intensive era, every industry wants to leverage data and build robust systems. However, this requires powerful GPU machines and HPCs.

High-performance computing systems (HPCs) are a collection of diverse yet powerful computing hardware that can accelerate resource-intensive projects and applications. The phrase “high-performance computing” work as an umbrella term. Enterprises should merge high-performance processing units to make their computation faster.

For creating high-performance computing (HPC) systems, enterprises must opt for the most appropriate GPU. This article will provide a comprehensive guide on HPC clusters, GPU machines, cloud GPU servers, and the top five GPUs for HPC workloads.

What are HPC Clusters?

HPC clusters are a bunch of potent interconnected computers that function as a single unit. They help enterprises solve complex computations quickly so that it reduces time to market. These HPC clusters are GPU machines performing large-scale parallel computations. Enterprises require massive amounts of processing power, memory, and storage for creating complex AI projects.

Researchers, engineers, scientists, and other enterprise professionals use HPC clusters that either run on cloud GPU servers or on-premise GPU machines. All HPC clusters work in tandem to deliver heavy-duty computations like ML-based analysis, deep learning modeling on images and videos, or innovating new ways to train machines.

· According to Hyperion’s research, the GPUs & hardware accelerator market is growing roughly at a CAGR of 26 percent every five years.
· Another research by Markets and Markets claims that the high-performance computing (HPC) market will grow from 36 billion USD in 2022 to 49.9 billion USD in 2027. It reflects a compound annual growth rate (CAGR) of 6.7 percent from 2022 to 2027.

HPC clusters typically consist of several nodes. Each node is a separate robust system connected to a high-speed network. These nodes comprise processors, memory, storage, etc., & are capable of performing computations independently. However, they work together within the HPC cluster to solve complex workloads and heavy-duty problems much faster than a single computer.

What are Graphical Processing Units (GPUs)?

Graphical Processing Units are specialized computer processors designed to handle applications having immense computational demands. They are highly parallel and optimized in terms of processing. For large numbers of complex mathematical operations simultaneously, enterprises & researchers prefer using GPUs.

GPUs contain hundreds or even thousands of cores that work simultaneously. It divides the tasks into smaller parts. Then it distributes those tasks among multiple GPU cores for faster and more efficient processing. They are ideal for rendering high-quality images, video encoding and decoding, AI/ML operations, and running complex simulations.

According to the Global Market Insight’s report, the GPU Market size was worth more than 40 billion USD in 2022. But with the increasing demand for complex computational power, it is witnessing a compound annual growth rate of 25 percent from 2023 to 2032. Hence, the projected value of this industry will reach 400 billion USD by 2032.

Why do enterprises need GPU machines bundled with high-performance computing systems?

GPU machines are robust GPU instances or cloud GPU servers. They contribute to specialized processing for rendering computationally-intensive workloads. GPU machines can be GPU clusters designed on-premise within an enterprise or cloud GPU servers hosted somewhere else and getting remotely accessed.

These machines help enterprises build complex applications such as deep learning models, scientific simulations, high-quality video rendering, image recognition algorithms, etc. In such applications, GPUs can significantly accelerate processing times compared to traditional CPU-based systems.

GPU machines leverage hundreds, if not thousands, of cores to offer exascale computing. Each GPU core can deliver a clock speed of 1.5 GHz. It means these GPU cores can execute 1.5 billion instructions per second. GPU machines are similar to traditional servers or virtual machines.

However, they come with one or more high-performance GPUs bundled to work as a single unit. The GPUs are typically powerful processors developed by companies like AMD and NVIDIA. Enterprises use them with HPCs to easily accommodate massive & concentrated process-centric operations.

Five Best Graphical Processing Units (GPUs) for High-performance Computing Workloads

High-performance computing (HPC) technology is more cost-effective than the traditional approach for supercomputing use cases. The acceleration of developing modern applications is possible because of the GPUs in HPC. Here is a list of the top five best GPUs for HPC workloads in 2023.

NVIDIA A30 GPU:

NVIDIA is known for its high-performance GPU manufacturing. The NVIDIA A30 GPU is a robust processing unit that offers powerful computing for HPC workloads. It runs on Ampere architecture. This energy-efficient GPU helps to enhance heavy-duty application development and remote AI/ML research without lagging. It offers conventional main memory with high-performance computing power for interactive ray-tracing and situational analysis.

Enterprises can also use A30 GPU for scientific applications, image recognition, and big data analytics workloads. It optimizes business operations by delivering 24 GB of GPU memory and a bandwidth of 933 gigabytes per second (GB/s). Using it, researchers and AI engineers can promptly decode double-precision calculations.

NVIDIA A100 GPU:

A100 is a popular NVIDIA GPU ideal for data centers and high-performance computing. It is a tensor-core GPU for deep learning and powering cutting-edge applications. Enterprises also use it for running AI/ML and deep learning research & development tasks. It established a deep learning GPU benchmark by AIME in 2022. We will discuss and list the GPU benchmark in the subsequent section.

NVIDIA A100’s multi-core technology can perform resource-intensive operations ten times quicker than regular GPUs. Enterprises can use it as a cloud GPU server or implement it on-premise for building HPC systems. It has 54 billion transistors and is, by far, the world’s largest GPU processor developed at the 7 nm (nanometer) range. Again, each core in the A100 GPU can provide 624 teraflops performance. It has a 1,555 GB memory bandwidth for accelerating HPC workloads.

NVIDIA V100 GPU:

If you are looking for a highly robust, enterprise-grade GPU for your HPC workload, this GPU is for you. This NVIDIA Volta architecture is one of the most advanced data center GPUs. It accelerates AI/ML projects and HPC workloads. It comes with 32 and 16 GB memory configurations. This beast is potent at delivering performance compared to 100 or more CPUs. It is another GPU that established a Deep Learning GPU benchmark in 2022, as reported by AIME.

NVIDIA V100 offers 640 tensor cores & went beyond the 100 teraflops barrier. It has a memory bandwidth of 1,134 GB per second and can process up to 130 teraflops capacity. HPCs can use it to develop powerful GPU machines because it can connect multiple V100 GPUs at up to 300 GB/s. Enterprises can use it for developing AI algorithms, ML training, data mining, deep neural network modeling, etc.

NVIDIA Tesla T4 GPU:

NVIDIA came up with Tesla T4 for next-level acceleration and improved AI. It is another robust enterprise-grade GPU for high-performance computing workloads and deep learning training. It combines low power consumption (ideally 70 watts) with 2560 shading units, 2560 Cuda cores, and 320 Tensor cores. It is a responsive GPU and can handle data in real-time. It offers a memory bandwidth of 300 GB per second and a processing capacity of 8.1 teraflops.

Realizing that future AI/ML apps will require resource-intensive computation, NVIDIA offered this fully diversified GPU. It can meet and exceed customer demand. Tesla T4 can accelerate cloud GPU servers and services. It enables HPC clusters to perform machine learning modeling, deep learning training and inference, data analytics, and rich graphics rendering.

QUADRO RTX 8000:

The QUADRO RTX series comes under the deep learning GPU benchmark. QUADRO RTX 8000 is another robust GPU that offers optimized and faster processing of 3D modeling, CAD, and ML projects. Enterprises integrate it to deploy and use high-performance computing systems. It delivers a GPU memory of 48 GB (GDDR6) & a memory bandwidth of up to 624 GB/s.

This GPU also offers 4609 Cuda cores, 576 Tensor cores, 119.4 TFLOPS of tensor performance, a base/boost core clock of 1440/1770 MHz, and delivers a power consumption of 250 watts. Enterprises can use it to build high-performance computing systems, simulation systems, virtual workstations, massive virtual reality projects, etc.

Deep learning GPU Benchmarks –

GPU benchmark is a professional assessment and evaluation technique to check the performance of GPU. The benchmarking checks enable enterprises to understand the GPUs’ ability to handle various compute-intensive workloads such as training ML algorithms, deep learning models, 3D rendering, video editing, data analysis, data mining, etc.

While setting a GPU benchmark, the process runs a series of tests and compares it with other GPUs. Then, they get a score as per their performance. The comparison depends on the relative performance of GPUs participating in the benchmark test.

Every year AIME releases a deep learning GPU benchmark list so that enterprises get a fair idea of which GPUs can render high-end operations. Here is a checklist of GPUs that successfully tilted them in the deep learning GPU benchmark. This is AIME 2022’s deep learning GPU benchmarks.

· GTX 1080TI

· Geforce RTX 2080TI

· QUADRO RTX 5000

· Geforce RTX 3090

· RTX A5000

· RTX A5500

· RTX A6000

· Geforce RTX 4090

· RTX 6000 Ada

· Tesla V100

· A100

· H100

Conclusion –

Orchestrating AI/ML projects and developing enterprise-grade HPCs for heavy-duty projects requires choosing the appropriate GPU. The right marrying of GPUs & high-performance computing architecture can help enterprises build AI/ML projects seamlessly. Hence, this article curated a list of the top & best GPUs that enterprises can use for HPC workloads.

If you want in-depth technical content or articles that will engage your B2B or B2C customers, contact me here. I write outstanding and versatile technical and non-technical content with infographics, animations, and SEO strategies that can bring potential leads & audiences to your website. You can ultimately enjoy more visibility and traffic on your website. My articles will contribute to your sales funnel and potential customer traction. The price/project or price per word is negotiable. So, what are you waiting for? Tap here and write me a mail today!