Imagine you have a team of powerful workers (the H200 GPUs). Each worker is very fast at math, building things, and solving problems — but one worker alone can only handle so much at a time.

If you have a huge project (like building a skyscraper or training a big AI model), one worker isn't enough. You need a team. You need to organize them and connect them.

🔧 How You Build the Team (the "Cluster")

• Get your workers (GPUs): Acquire several H200s (or similar high-performance GPUs). These are your core computational units.
• Give them a workspace (Servers): Install the GPUs into host computers (servers), providing power, cooling, and basic connectivity.
• Connect them with highways (Fast Interconnects): Use high-speed networking like NVLink (for GPU-to-GPU within a server) or InfiniBand/Ethernet (for server-to-server communication) to allow rapid data exchange.
• Pick a team leader (Head/Control Node): Designate one or more computers to manage the cluster, schedule jobs, and provide user access.
• Use a work schedule (Cluster Management Software): Install software (like Kubernetes with GPU support, Slurm, or NVIDIA's management tools) to allocate resources, manage workloads, and monitor performance.

🚀 Why Build a Cluster?

Together, your GPUs can tackle massive computational tasks that a single machine cannot handle efficiently. It's the difference between building a skyscraper with a single worker versus a coordinated construction crew. You can train larger AI models faster, run complex simulations, or process vast datasets in parallel.

🏗️ Analogy: Building Crew