GPU Cluster in 2026: Key Things to Know & Use Cases
A GPU cluster is a set of computers where each node is equipped with a Graphics Processing Unit (GPU). Computational demands are ever-rising, whether in cloud or traditional markets. In this context, having the right knowledge on GPU clusters is more important than before.
Explore how to form GPU clusters, identify some of the top vendors and learn real-life use cases of GPU clusters.
The components of GPU clusters
The components of a GPU cluster can be divided into hardware and software categories. The classification of GPU cluster hardware is divided into two distinct types: heterogeneous and homogeneous.
Heterogeneous cluster: In this category, hardware from both major independent hardware vendors (IHVs), (e.g. AMD,NVIDIA or other AI hardware brands), can be utilized. This classification also applies if different models of the same GPU brand are mixed within the cluster.
Homogeneous cluster: This type refers to a GPU cluster where every single GPU is identical in terms of hardware class, brand, and model.
Key hardware components
1. Component-level hardware
- GPUs (Graphics Processing Units): The core components of a GPU cluster, designed for parallel processing of complex calculations. GPUs are commonly used in tasks such as machine learning, scientific simulations, and rendering. Leading-edge examples include the AMD Instinct MI400-series (MI430X/MI455X), which provide the massive FLOPS required for yotta-scale AI training.
- CPUs (Central Processing Units): These act as the cluster’s “brain” for serial tasks and data management. Modern clusters pair high-performance GPUs with specialized CPUs, such as the AMD EPYC “Venice” line, to prevent data bottlenecks.
- Memory (HBM & RAM): High-Bandwidth Memory (HBM3e/HBM4) is integrated directly onto the GPU for lightning-fast data access, while traditional RAM supports the CPU’s system operations.
- Networking hardware: Includes SmartNICs and DPUs (like the Pensando “Vulcano”). These are essential for the communication between nodes that allows hundreds of GPUs to work as a single machine.
- Storage devices (NVMe SSDs): High-speed storage is required to feed massive datasets to the GPUs without awaiting GPU for data.
2. System-level architecture (Rack-scale)
- Integrated AI platforms: Modern clusters are increasingly moving away from individual servers toward Rack-Scale Architecture, such as AMD’s Helios platform.
- Helios serves as a blueprint for massive infrastructure, integrating up to 72 GPUs, specialized CPUs, and networking into a single, cohesive unit.
- These systems are designed to reach Exascale performance (delivering up to 3 exaflops in a single rack), utilizing advanced inter-chip fabrics to maximize data transfer speeds between all components.
3. Infrastructure & Support
- Power supply units (PSUs): High-density clusters require massive amounts of electrical power. PSUs provide the necessary electrical power to all components, and their reliability and efficiency are crucial for the stable operation of the cluster.
- Cooling systems: GPUs and CPUs generate significant heat, and effective cooling is necessary to prevent overheating and ensure long-term reliability. Liquid cooling (Direct-to-Chip or Immersion) can help maintain stability and prevent thermal throttling.
Key software components
- Operating system: The foundational software that manages the hardware resources and provides the environment for running other software. Almost exclusively Linux (Ubuntu, RHEL, or Rocky) due to superior driver support and containerization capabilities.
- GPU drivers and runtimes: The software layer (e.g., NVIDIA Driver + NVIDIA Container Toolkit) that allows the OS and containers to communicate with the hardware.
- Parallel computing platforms: Libraries like CUDA, ROCm or OpenCL allow developers to use GPUs for parallel processing tasks. CUDA is especially well-known for NVIDIA GPUs, providing libraries for developers.
- Security software: They mainly guarantee the protection of the cluster from external threats.
- Monitoring and observability: Tools like Prometheus and Grafana (often paired with DCGM) to track GPU temperature, power draw, and memory usage in real-time.
- Communication libraries: Tools like NCCL (NVIDIA Collective Communications Library) that optimize how data is shared across multiple GPUs and nodes during a single task.
- Cluster scheduling & orchestration: Schedulers ensure fair access and maximum hardware utilization. For example:
- Slurm (Simple Linux Utility for Resource Management) remains the dominant scheduler in traditional HPC/research clusters, focusing on job queues.
- Kubernetes (with NVIDIA’s GPU plugin) is the industry standard for large-scale AI workloads in production, treating GPUs as scalable cloud-native resources.
- New AI-oriented schedulers (e.g. Run:AI) exist, but mainstream deployments still rely on these established frameworks.
How does a GPU cluster operate?
GPU clusters use multiple GPUs to perform complex computations more efficiently than traditional CPUs. They rely on parallel processing (as with AI chips) and efficient data handling.
Parallel processing
Task division: In a GPU cluster, large computational tasks are divided into smaller sub-tasks. This division is based on the nature of the task and its suitability for parallel processing.
Simultaneous execution: Each GPU in the cluster processes its assigned sub-tasks simultaneously. Contrary to CPUs, which typically have a smaller number of cores optimized for sequential serial processing, GPUs have hundreds or thousands of smaller cores designed for parallel processing. This architecture allows them to handle multiple operations simultaneously.
Speed and efficiency: This parallelism significantly speeds up processing, especially for tasks like image processing, scientific simulations, or machine learning algorithms, which involve handling large amounts of data or performing similar operations repeatedly.
Data handling
Data distribution: Data to be processed is distributed across the GPUs in the cluster. Efficient data distribution is crucial to ensure that each GPU has enough work to do without causing data bottlenecks.
Memory usage: Each GPU has its own memory (VRAM), which is used to store the data it is currently processing. Efficient memory management is vital to maximize the throughput and performance of each GPU.
Data transfer: Data transfer between GPUs, or between GPUs and CPUs, is managed via the cluster’s high-speed interconnects. This transfer needs to be fast to minimize idle time and ensure that all units are efficiently utilized.
A practical example:
Consider a task like rendering a complex 3D scene, a typical operation in computer graphics and animation:
- The scene is divided into smaller parts or frames.
- Each GPU works on rendering a part of the scene simultaneously.
- The rendered parts are then combined to form the final scene.
Steps to build a GPU cluster
Requirement assessment: Determine the purpose of the GPU cluster (e.g., AI, scientific computing) and assess the computational needs. This will guide the scale and specifications of the cluster.
Hardware selection: Select the right hardware components mentioned above (Figure 1) according to your needs.
Cluster design: Decide on the number of nodes (individual computers) in the cluster. Each node will house one or more GPUs. Plan the physical layout considering space, power, and cooling requirements.
Assembly: Assemble the hardware components. This includes installing CPUs, GPUs, memory, and storage on the motherboards and mounting them in a rack or enclosure.
Networking: Connect the nodes using the networking hardware. Ensure high bandwidth and low latency for efficient communication between nodes.
Software installation: Install an operating system, typically a Linux distribution for its robustness in cluster environments. Install GPU drivers, necessary libraries (like CUDA for NVIDIA GPUs), and cluster management software.
Configuration and testing: Configure the network settings, cluster management tools, and distributed computing frameworks. Test the cluster for stability, performance, and efficiency. This may include running benchmarking tools and stress tests.
6 real-world GPU cluster use cases
1- Google brain project
Google has implemented GPU clusters for its Google Brain project, which focuses on deep learning and artificial intelligence research. These clusters are used to train neural networks for applications such as image and speech recognition, improving the capabilities of services like Google Photos and Google Assistant.1
2- Weather forecasting at the National Oceanic and Atmospheric Administration (NOAA)
NOAA uses GPU clusters for high-resolution climate and weather modeling. These clusters enable faster and more accurate weather forecasts by rapidly processing big datasets, crucial for predicting severe weather events and understanding climate change impacts.2
3- Risk analysis and algorithmic trading
Major investment banks and financial institutions employ GPU clusters for complex risk analysis and algorithmic trading. These clusters can process vast amounts of market data in real-time; allow rapid trading decisions and advanced financial modeling to maximize returns and mitigate risks.3
4- Large hadron collider (LHC) at CERN
The European Organization for Nuclear Research (CERN) utilizes GPU clusters to process data from the Large Hadron Collider. These clusters help in analyzing the vast amounts of data generated by particle collisions, aiding in discoveries in particle physics, like the Higgs boson detection.4
5- Pharmaceutical research and drug discovery
Pharmaceutical companies leverage GPU clusters for molecular dynamics simulations, critical in drug discovery and development. These simulations help understand drug interactions at the molecular level, speeding up the development of new medications and therapies.5
6- Government and research computing
The U.S. DOE announced two new national AI supercomputers at Argonne National Lab: “Solstice” and “Equinox.” Solstice will include 100,000 NVIDIA Blackwell GPUs and Equinox 10,000 GPUs, and the two systems together will deliver approximately 2,200 exaflops of AI performance.6
These GPU-based supercomputers are being built in partnership between the DOE, NVIDIA and Oracle.
Top companies in GPU cluster area
Note that: Table consists of only representative examples of systems built by each vendor in certain filters. For NVIDIA GPU architecture, table focuses on NVIDIA’s ready-made system offerings. 7 Apart from NVIDIA, providers such as Lambda also offer cluster system/structures on cloud. 8
For more solutions, check out Cloud GPU Platform.
Businesses and individuals can also build their on premise clusters through NVIDIA, SuperPOD and BasePOT
*Vendors are sorted according to their market value.9
GPU clusters vs CPUs
Computational architecture: GPU clusters are designed for parallel processing. This makes them fit for tasks involving large data sets or computations that can be performed in parallel. Traditional CPU clusters, on the contrary, are better suited for sequential task processing.
Performance: For tasks that can be parallelized, GPU clusters typically offer better stability than CPU clusters.10
Application suitability: GPU clusters excel in areas like deep learning, scientific simulations, and real-time data processing, whereas CPU clusters are often preferred for general-purpose computing and tasks requiring high single-threaded performance.
Are GPU clusters costly to form?
Initial setup costs: High upfront investment in purchasing GPUs, which are generally more expensive than CPUs. Additional costs include networking equipment, storage, cooling systems, and power supplies.
Operational costs: Significant power and cooling requirements lead to higher ongoing operational costs compared to CPU clusters.
Maintenance and upgrade Costs: Regular maintenance and potential upgrades for keeping up with technological advancements can add to the total cost of ownership.
GPU clusters can be costly. Yet, data shows that the general throughput trends for GPU clusters differ from CPU clusters in that adding more GPU nodes to the cluster increases throughput performance linearly.11 In that sense, GPU clusters can perform with more stability with lover costs compared to CPUs.
Learn more on cloud cost management tools.
What security considerations to know about GPU clusters?
Data security: Ensuring the confidentiality and integrity of data processed within the cluster can be especially important for sensitive information in fields like healthcare and finance.
Network security: Protection against external threats is also crucial, given the high volume of data transfer within and outside the cluster.
Physical security: Securing the physical hardware against theft, tampering, or damage, especially in shared or public environments like data centers.
Software security: Regular updates and patches for the operating system, drivers, and other software components to protect against vulnerabilities.
FAQ
A GPU kernel is a small program that runs on a GPU and executes many parallel threads to process data quickly. It’s commonly used in AI, scientific computing, and graphics tasks.
In the context of GPU clusters, GPU kernels are the basic units of work distributed across the cluster. Each GPU in the cluster runs many instances of the kernel in parallel, allowing massive workloads to be processed efficiently.
For example, training a large AI model uses a GPU cluster to run GPU kernels simultaneously on different chunks of data, significantly speeding up computation.
Further reading:
- Cloud Inference: 3 Powerful Reasons to Use
- Oracle Cloud Infrastructure: Azure & OpenAI’s Preferred Cloud
- Comparison of Top Free Cloud GPU Services
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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