AI is growing rapidly, driven by advancements in generative and agentic AI. This growth has created a significant demand for computational power that traditional infrastructure cannot meet. GPUs, originally designed for graphics rendering, are now essential for training and deploying modern AI models.
To keep up with large datasets and complex computations, organizations are turning to GPU clusters. These clusters use parallel processing to handle workloads more efficiently, reducing the time and resources needed for training and inference. Single GPUs are often not enough for the scale required today.
Agentic AI also increases the need for high-performance, low-latency computing. These systems require real-time, context-aware processing, which GPU clusters can support effectively. Businesses that adopt GPU clusters early can accelerate their AI development and deliver new solutions to the market faster than those using less capable infrastructure.
In this blog, we will explore what GPU clusters are, the key components that make them up, how to create your own cluster for your AI workloads, and how to choose the right GPUs for your specific requirements.
Quick summary: GPU clusters connect many GPUs to provide the memory bandwidth and parallelism required for modern AI workloads. The data‑center GPU market is growing ~35 % per year and accelerated computing can reduce energy use by 20–50× compared with CPU‑only servers
A GPU cluster is an interconnected network of computing nodes, each equipped with one or more GPUs, along with traditional CPUs, memory, and storage components. These nodes work together to handle complex computational tasks at speeds far surpassing those achievable by CPU-based clusters. The ability to distribute workloads across multiple GPUs enables large-scale parallel processing, which is critical for AI workloads.
GPUs achieve parallel execution through their architecture, with thousands of smaller cores capable of working on different parts of a computational problem simultaneously. This is a stark contrast to CPUs, which handle tasks sequentially, processing one instruction at a time.
| Attribute | CPU server | GPU cluster |
|---|---|---|
| Parallelism | Few cores; optimized for sequential logic | Thousands of cores; optimized for massively parallel tasks |
| Memory bandwidth | ~50 GB/s for typical server | Up to 7.8 TB/s for modern GPUs；the latest B200 has 8 TB/s |
| Use‑cases | General‑purpose computing | Deep learning, data analytics, graph processing |
| Cost efficiency | Lower upfront cost; less efficient for training | Higher capital cost but 10×+ throughput for AI |
Efficient operation of a GPU cluster depends on high-speed networking interconnects, such as NVLink, InfiniBand, or Ethernet. These high-speed channels are essential for rapid data exchange between GPUs and nodes, reducing latency and performance bottlenecks, particularly when dealing with massive datasets.
GPU clusters play a vital role across various stages of the AI lifecycle:
Model Training: GPU clusters are the primary infrastructure for training complex AI models, especially large language models, by processing massive datasets efficiently.
Inference: Once AI models are deployed, GPU clusters provide high-throughput and low-latency inference, critical for real-time applications requiring quick responses.
Fine-tuning: GPU clusters enable the efficient fine-tuning of pre-trained models to adapt them to specific tasks or datasets.
Quick Summary: A GPU cluster is a group of nodes equipped with multiple GPUs interconnected by low‑latency links. Compared to CPU servers (≈50 GB/s memory bandwidth), GPUs provide up to 7.8 TB/s bandwidth; this makes them roughly 10× faster for deep‑learning workloads
A common challenge in managing GPU clusters is addressing the varying resource demands of different AI workloads. Some tasks require the full computational power of a single GPU, while others can operate efficiently on a fraction of that capacity. Without proper resource management, GPUs can often be underutilized, leading to wasted computational resources, higher operational costs, and excessive power consumption.
GPU fractioning addresses this by allowing multiple smaller workloads to run concurrently on the same physical GPU. In the context of GPU clusters, this technique is key to improving utilization across the infrastructure. It enables fine-grained allocation of GPU resources so that each task gets just what it needs.
This approach is especially useful in shared clusters or environments where workloads vary in size. For example, while training large language models may still require dedicated GPUs, serving multiple inference jobs or tuning smaller models benefits significantly from fractioning. It allows organizations to maximize throughput and reduce idle time across the cluster.
Clarifai’s Compute Orchestration simplifies the process of scheduling and resource allocation, making GPU fractioning easier for users. For more details, check out the detailed blog on GPU fractioning.
A GPU cluster brings together hardware and software to deliver the compute power needed for large-scale AI. Understanding its components helps in building, operating, and optimizing such systems effectively.
The head node is the control center of the cluster. It manages resource allocation, schedules jobs across the cluster, and monitors system health. It typically runs orchestration software like Kubernetes, Slurm, or Ray to handle distributed workloads.
Worker nodes are where AI workloads run. Each node includes one or more GPUs for acceleration, CPUs for coordination, RAM for fast memory access, and local storage for operating systems and temporary data.
GPUs are the core computational units, responsible for heavy parallel processing tasks.
CPUs handle system orchestration, data pre-processing, and communication with GPUs.
RAM supports both CPUs and GPUs with high-speed access to data, reducing bottlenecks.
Storage provides data access during training or inference. Parallel file systems are often used to meet the high I/O demands of AI workloads.
Operating Systems (commonly Linux) manage hardware resources.
Orchestrators like Kubernetes, Slurm, and Ray handle job scheduling, container management, and resource scaling.
GPU Drivers & Libraries (e.g., NVIDIA CUDA, cuDNN) enable AI frameworks like PyTorch and TensorFlow to access GPU acceleration.
Fast networking is critical for distributed training. Technologies like InfiniBand, NVLink, and high-speed Ethernet ensure low-latency communication between nodes. Network Interface Card (NICs) with Remote Direct Memory Access (RDMA) support help reduce CPU overhead and accelerate data movement.
Efficient storage plays a critical role in high-performance model training and inference, especially within GPU clusters used for large-scale GenAI workloads. Rather than relying on memory, which is both limited and expensive at scale, high-throughput distributed storage allows for seamless streaming of model weights, training data, and checkpoint files across multiple nodes in parallel.
This is essential for restoring model states quickly after failures, resuming long-running training jobs without restarting, and enabling robust experimentation through frequent checkpointing.
Clarifai’s Compute Orchestration simplifies the complex task of provisioning, scaling, and managing GPU infrastructure across multiple cloud providers. Instead of manually configuring virtual machines, networks, and scaling policies, users get a unified interface that automates the heavy lifting—freeing them to focus on building and deploying AI models. The platform supports major providers like AWS, GCP, Oracle, and Vultr, giving flexibility to optimize for cost, performance, or location without vendor lock-in.
Here’s how to create a GPU cluster using Clarifai's Compute Orchestration:
Within the Clarifai UI, go to the Compute section and click New Cluster.
You can deploy using either Dedicated Clarifai Cloud Compute for managed GPU instances, or Dedicated Self-Managed Compute to use your own infrastructure, which is currently in development and will be available soon.
Next, select your preferred cloud provider and deployment region. We support AWS, GCP, Vultr, and Oracle, with more providers being added soon.
Also select a Personal Access Token, which is required to authenticate when connecting to the cluster.
Next, define a Nodepool, which is a set of compute nodes with the same configuration. Specify a Nodepool ID and set the Node Auto-Scaling Range, which defines the minimum and maximum number of nodes that can scale automatically based on workload demands.
For example, you can set the range between 1 and 5 nodes. Setting the minimum to 1 ensures at least one node is always running, while setting it to 0 eliminates idle costs but may introduce cold start delays.
Then, select the instance type for deployment. You can choose from various options based on the GPU they offer, such as NVIDIA T4, A10G, L4, and L40S, each with corresponding CPU and GPU memory configurations. Choose the instance that best fits your model's compute and memory requirements.
For more detailed information on the available GPU instances and their configurations, check out the documentation here.
Finally, deploy your model to the dedicated cluster you've created. You can choose a model from the Clarifai Community or select a custom model you've uploaded to the platform. Then, pick the cluster and nodepool you've set up and configure parameters like scale-up and scale-down delays. Once everything is configured, click "Deploy Model."
Clarifai will provision the required infrastructure on your selected cloud and handle all orchestration behind the scenes, so you can immediately begin running your inference jobs.
If you'd like a quick tutorial on how to create your own clusters and deploy models, check this out!
Clarifai currently supports GPU instances for inference workloads, optimized for serving models at scale with low latency and high throughput. Selecting the right GPU depends on your model size, latency requirements, and traffic scale. Here’s a guide to help you choose:
For tiny models (e.g., <2B LLMs like Qwen3-0.6B or typical computer vision tasks), consider using T4 or A10G GPUs.
For medium-sized models (e.g., 7B to 14B LLMs), L40S or higher-tier GPUs are more suitable.
For large models, use multiple L40S, A100, or H100 instances to meet compute and memory demands.
Support for training and fine-tuning models will be available soon, allowing you to leverage GPU instances for those workloads as well.
Choosing the appropriate GPU depends on model size, batch sizes and budget. A comparison of key specifications illustrates the trade‑offs:
| Model | Memory (HBM) | Bandwidth | Peak Compute | Key benefit | Price & notes |
|---|---|---|---|---|---|
| A100 (40 / 80 GB) | 40/80 GB HBM2 | 1.6 TB/s | ~312 TFLOPS FP16 | Solid baseline for medium‑scale training and inference; widely available | $$—lowest cost in this table; fractional options common |
| H100 | 80 / 94 GB HBM3 | 3.4 TB/s | ~700 TFLOPS FP16 | Offers ~2× bandwidth and compute over A100; good for larger LLMs and vision models | $$; MIG allows splitting into seven instances |
| H200 | 141 GB HBM3 | 4.8 TB/s | ~1.4 PFLOPS FP8 | Increased memory to fit bigger context windows; ideal for retrieval‑augmented generation (RAG) | $$$; limited availability |
| B200 | 192 GB HBM3e | 8 TB/s | 20 PFLOPS FP4 | Second‑generation Transformer Engine; fits GPT‑4‑class 400B parameter models on a single card and offers 2–3× tokens/s vs H100 | $$$$; card costs ≈$30k–$40k |
Prompt‑ready summary: Pick GPUs based on model size and budget. A100s suit smaller models; H100s balance cost and performance; H200s offer more memory; B200s provide 192 GB HBM3e, 8 TB/s bandwidth, and 20 PFLOPS compute, enabling 400‑B‑parameter models on one card but at higher cost
GPU clusters power a wide range of industries and scientific domains. Some notable examples include:
These examples highlight how GPU clusters transform industries by enabling computationally intensive tasks that were previously impractical or cost‑prohibitive.
Quick summary: GPU clusters enable breakthroughs across domains—from weather forecasting and financial risk analysis to particle physics and drug discovery. Their throughput scales almost linearly with nodes, making them indispensable for large‑scale simulations and deep learning.
Running large GPU clusters introduces significant power and cooling requirements. Data centres already consume 1–2 % of global electricity, and AI workloads can exacerbate this. NVIDIA CEO Jensen Huang emphasizes that accelerated computing can save 20–50× more energy compared with CPU‑only systems. Advances in transistor design and algorithms have improved energy efficiency for AI inference by 45,000× over eight years.
New GPUs like the B200 consume around 700 W per card. Organizations must account for electrical infrastructure, cooling, and floor space. Direct‑to‑chip liquid cooling is increasingly adopted to reduce energy consumption and noise. When considering on‑premises ownership, the cost of a B200 DGX system ($515,000 for 8 GPUs) should be weighed against cloud pricing ($6–14/hour per GPU). Break‑even occurs at about 60% utilization over 18 months, excluding electricity and staff costs.
Quick summary: GPU clusters demand significant power and cooling. Accelerated computing can be 20–50× more energy‑efficient than CPU‑only systems. The B200 consumes around 700 W per GPU; liquid cooling and careful utilization planning (e.g., break‑even at ~60% utilization over 18 months) are critical for cost control.
Industry reports illustrate the rapid adoption of GPU clusters:
Quick summary: The data‑center GPU market is projected to grow from USD 14.48 B in 2024 to USD 190 B by 2033. Orchestration and GPU‑as‑a‑service markets are also expanding rapidly, reflecting widespread adoption across enterprises.
Deploying a GPU cluster successfully requires attention to several factors:
Quick summary: Design high‑bandwidth networks, use intelligent schedulers, optimise data pipelines and implement strong governance. Monitor utilisation to avoid over‑provisioning and favour fractional GPUs when possible.
| Model | Pros | Cons | Suitable scenarios |
|---|---|---|---|
| On‑prem clusters | Full control; potential cost savings at high utilisation; compliance with data residency | High upfront capex; requires specialised staff; electricity & cooling overhead | Established enterprises with consistent, large workloads and strict compliance needs |
| Cloud GPU instances | Elastic scaling; pay‑as‑you‑go; managed infrastructure | Higher per‑hour cost; dependent on cloud availability; possible vendor lock‑in | Startups, bursty workloads, experimentation |
| GPU‑as‑a‑Service (serverless) | Rapid provisioning; per‑second billing; no cluster management | Limited customisation; restricted hardware selection; performance variability | Prototyping, intermittent inference workloads |
Quick summary: Choose on‑prem clusters for consistent, high‑utilisation workloads; cloud instances for flexible scaling; and GPU‑as‑a‑service for quick experiments and bursty inference.
Before launching or expanding your GPU infrastructure, consult the following checklist:
Quick summary: Use this checklist to plan GPU clusters: specify workloads, pick appropriate GPUs, design networks and storage, choose orchestration, enable auto‑scaling and fractional GPUs, monitor utilisation, budget for cooling and power, and enforce security policies
GPU clusters are essential for meeting the computational demands of modern AI, including generative and agentic applications. They enable efficient model training, high-throughput inference, and fast fine-tuning, which are key to accelerating AI development.
Clarifai’s Compute Orchestration simplifies the deployment and management of GPU clusters across major cloud providers. With features like GPU fractioning and auto-scaling, it helps optimize resource usage and control costs while allowing teams to focus on building AI solutions instead of managing infrastructure.
If you are looking to run models on dedicated compute without vendor lock-in, Clarifai offers a flexible and scalable option. To request support for specific GPU instances not yet available, please contact us.
Q1. What is a GPU cluster and why is it important for AI?
A GPU cluster is a group of interconnected servers (nodes), each containing one or more GPUs, CPUs, memory, and storage. They allow workloads to run in parallel across thousands of GPU cores, making them essential for training and deploying modern AI models at scale.
Q2. How do GPU clusters accelerate AI workloads compared to CPUs?
GPUs excel at parallel processing, performing thousands of operations simultaneously. This makes them 10× faster or more than CPUs for tasks like deep learning and generative AI.
Q3. What is GPU fractioning and why does it matter?
GPU fractioning (Multi-Instance GPU) splits a single GPU into smaller virtual slices. This allows multiple smaller jobs to share the same GPU efficiently, reducing idle time and lowering costs without sacrificing performance.
Q4. What are the main components of a GPU cluster?
Head node – manages scheduling and orchestration.
Worker nodes – run GPU workloads with CPUs, RAM, and storage.
Networking – high-speed interconnects like NVLink or InfiniBand to reduce latency.
Storage layer – parallel file systems and distributed storage for high-throughput training and inference.
Q5. How can I create a GPU cluster easily?
Platforms like Clarifai Compute Orchestration simplify the process. You can:
Create a cluster on AWS, GCP, Oracle, or Vultr.
Define node pools and auto-scaling ranges.
Deploy your model with a few clicks—orchestration handles scaling and monitoring behind the scenes.
Q6. Which GPUs should I choose for my workload?
Tiny models (<2B parameters): NVIDIA T4, A10G.
Medium models (7–14B): L40S or higher.
Large models (>70B): Multi-GPU setups with A100 or H100.
The right GPU depends on your model size, latency requirements, and traffic scale.
Q7. Can GPU clusters be used for inference, or only training?
Both. Clusters not only train large models but also serve them in production, delivering high-throughput, low-latency inference for applications like chatbots, vision systems, or recommendation engines.
Q8. How do GPU clusters help with cost efficiency?
By using auto-scaling, GPU fractioning, and cloud orchestration, you only pay for resources when needed. This helps avoid idle GPU costs and optimizes utilization.
Q9. Are GPU clusters only for big enterprises?
No. Startups and SMEs can leverage GPU-as-a-Service via cloud providers to access cutting-edge GPUs without massive upfront investment.
Q10. What’s the future of GPU clusters?
With increasing model sizes and real-time AI demands, clusters will adopt new GPUs like the B200 (192GB HBM3e, 8 TB/s bandwidth), better cooling (liquid and immersion), and smarter orchestration. Expect more fractioned, multi-cloud, and energy-efficient clusters in the coming years.
