Month: May 2025

Introduction

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.

What is a GPU Cluster?

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.

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.

The Significance of GPU Fractioning

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.

Key Components of a GPU Cluster

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.

Head Node

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

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.

Hardware

• 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.

Software Stack

• 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.

Networking

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.

Storage Layer

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.

Creating GPU Clusters with Clarifai

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:

Step 1: Create a New Cluster

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.

Step 2: Define Node Pools and Configure Auto-Scaling

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.

Step 3: Deploy

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!

Choosing the Right GPUs for your Needs

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.

Conclusion

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.