What is GPU Cluster?
A GPU cluster is a group of multiple GPUs connected through high-speed networking that work together as a unified system to train large AI models, enabling organisations to distribute massive computational workloads across many processors to dramatically reduce training time.
What Is a GPU Cluster?
A GPU cluster is a collection of multiple GPU-equipped servers linked by high-speed interconnects that function as a single computational resource. While a single GPU can handle many AI tasks, training large-scale models, such as large language models, complex recommendation systems, or advanced computer vision models, requires more computational power than any single GPU can provide. A GPU cluster distributes this work across dozens, hundreds, or even thousands of GPUs working in parallel.
The largest AI models in the world, including the foundation models behind ChatGPT, Claude, and Google Gemini, were trained on GPU clusters containing thousands of chips. While most businesses will never need clusters of that scale, understanding GPU clusters is important for any organisation considering serious AI development or fine-tuning of large models.
How GPU Clusters Work
A GPU cluster involves several key components working together:
GPU Servers (Nodes)
Each node in the cluster is a server containing multiple GPUs. A typical enterprise GPU node might contain four to eight NVIDIA A100 or H100 GPUs. The GPUs within a single node communicate through NVLink, NVIDIA's high-bandwidth interconnect that enables data transfer at speeds far exceeding standard network connections.
Cluster Networking
Connecting nodes together requires specialised high-speed networking:
- InfiniBand: The gold standard for GPU cluster networking, providing ultra-low latency and high bandwidth between nodes. NVIDIA's ConnectX adapters and Quantum switches are the most common InfiniBand solutions.
- High-speed Ethernet: RoCE (RDMA over Converged Ethernet) provides a lower-cost alternative that delivers good performance for many workloads.
The network is often the bottleneck in GPU cluster performance. During distributed training, GPUs across different nodes must constantly synchronise their calculations, and slow networking creates idle time that wastes expensive GPU capacity.
Distributed Training Software
Software frameworks coordinate how the training workload is split across GPUs:
- Data parallelism: The same model is copied to each GPU, and each processes a different subset of the training data. Results are synchronised periodically.
- Model parallelism: Different parts of the model are placed on different GPUs, used when a model is too large to fit on a single GPU.
- Pipeline parallelism: Different stages of the model are processed on different GPUs in a pipelined fashion.
Popular frameworks for distributed training include PyTorch Distributed, DeepSpeed (Microsoft), Megatron-LM (NVIDIA), and JAX with GSPMD (Google).
Cluster Management
Software for scheduling jobs, allocating GPUs, and monitoring cluster health:
- SLURM: The most common job scheduler for GPU clusters
- Kubernetes with GPU operators: Container-based cluster management
- Cloud-managed clusters: AWS, Google Cloud, and Azure offer managed GPU cluster services
Why GPU Clusters Matter for Business
Most SMBs in Southeast Asia will not build or operate their own GPU clusters. However, understanding GPU clusters is important because they underpin the AI services and capabilities businesses depend on:
Training Custom Models at Scale
Organisations that need to train large custom models, such as a financial services company building a specialised risk model on billions of transactions, may need GPU cluster capacity. Cloud providers offer this on demand without the need to purchase hardware.
Fine-Tuning Foundation Models
Fine-tuning large language models or vision models for specific business applications often requires multi-GPU setups. While not always a full cluster, understanding distributed computing concepts helps teams plan and budget these projects effectively.
Cost Optimisation
GPU compute is expensive. Understanding how clusters work helps business leaders make informed decisions about:
- Whether to use cloud GPU clusters on demand or invest in dedicated capacity
- How to choose between GPU types for different workloads
- When distributed training is necessary versus when a single GPU suffices
Accessing GPU Clusters
For businesses in Southeast Asia, several options exist:
Cloud GPU Clusters
- AWS: P4d and P5 instances with NVIDIA A100/H100 GPUs, available in ap-southeast-1 (Singapore)
- Google Cloud: A3 instances with H100 GPUs, available in asia-southeast1 (Singapore)
- Azure: ND-series instances with H100 GPUs, with regional availability in Southeast Asia
- Lambda Cloud and CoreWeave: Specialised GPU cloud providers often offering competitive pricing
Managed ML Platforms
- AWS SageMaker: Managed distributed training across GPU clusters
- Google Vertex AI: Managed training with automatic cluster provisioning
- Databricks: Managed ML platform with GPU cluster support
On-Premise Clusters
For organisations with sustained, large-scale GPU needs and the technical capacity to operate them, building an on-premise cluster can be cost-effective over two to three years. However, this requires significant upfront investment and specialised staff.
Planning GPU Cluster Usage
For organisations considering GPU cluster workloads:
- Validate the need: Many AI tasks, including fine-tuning smaller models, training traditional ML models, and running inference, do not require a cluster. A single GPU or small multi-GPU server is often sufficient.
- Start with cloud: Use on-demand cloud GPU clusters to determine your actual requirements before considering dedicated infrastructure.
- Optimise before scaling: Ensure your training code is efficient before adding more GPUs. Poorly optimised code on a larger cluster wastes money without proportional speedup.
- Consider spot and preemptible instances: Cloud providers offer significant discounts on interruptible GPU instances, which work well for training jobs that can checkpoint and resume.
- Budget carefully: GPU cluster costs can be substantial. A cluster of 8 NVIDIA H100 GPUs on cloud infrastructure can cost $20-30 per hour. Multi-day training runs add up quickly.
GPU clusters represent the high end of AI infrastructure. While not every organisation needs them, understanding their role in the AI ecosystem helps business leaders make informed decisions about when to invest in large-scale compute and when simpler alternatives will suffice.
GPU clusters represent the most significant infrastructure investment in AI, and CEOs and CTOs need to understand when this investment is justified versus when simpler alternatives suffice. The cost difference is dramatic: training a model on a single GPU might cost $50, while the same model trained faster on a GPU cluster might cost $500, and a large-scale training job on a multi-node cluster can easily run into tens of thousands of dollars.
For business leaders in Southeast Asia, the key strategic decision is between cloud GPU clusters and dedicated infrastructure. Cloud clusters from AWS and Google Cloud are available on demand in Singapore, eliminating the need for massive capital expenditure. This is the right choice for most organisations, providing flexibility to scale up for intensive training periods and scale down when compute is not needed.
The business case for GPU cluster usage typically involves custom model development that provides competitive advantage, not routine AI tasks that can be served by pre-trained models or APIs. Before approving GPU cluster budgets, ask whether the custom model being trained will deliver meaningfully better results than existing pre-trained alternatives. If the answer is yes, cloud GPU clusters provide the most cost-effective and flexible path for organisations in the region.
- Validate that your workload actually requires a GPU cluster. Many AI tasks, including inference, small model training, and fine-tuning, run effectively on a single GPU or small multi-GPU server.
- Start with cloud GPU clusters rather than purchasing hardware. Cloud provides flexibility and eliminates the risk of expensive hardware sitting idle.
- Use spot or preemptible GPU instances for training workloads to reduce costs by 60-70%. Implement checkpointing so training can resume if instances are interrupted.
- Optimise your training code before scaling to more GPUs. Doubling GPUs does not double performance if the code has communication bottlenecks.
- Budget carefully and set spending alerts. Multi-day training runs on GPU clusters can generate substantial bills that compound quickly.
- Choose cloud regions in Southeast Asia, particularly Singapore, for GPU cluster access with low latency and data residency compliance.
- Consider managed ML platforms like SageMaker or Vertex AI that handle cluster provisioning and management, reducing the need for specialised infrastructure expertise.
Frequently Asked Questions
How much does it cost to use a GPU cluster in the cloud?
Cloud GPU cluster costs vary significantly by GPU type and provider. A single NVIDIA H100 GPU instance costs approximately $3-4 per hour on demand. A cluster of 8 H100s would cost $24-32 per hour, or roughly $576-768 per day. A multi-day training job on a 32-GPU cluster could cost $5,000-10,000 or more. Spot instances can reduce these costs by 60-70% but may be interrupted. Most SMBs spend $1,000-10,000 per month on GPU compute, while larger enterprises may spend $50,000 or more for intensive model development.
When does a business need a GPU cluster versus a single GPU?
A single GPU suffices for most inference workloads, training traditional ML models, fine-tuning small to medium models, and prototyping. A GPU cluster becomes necessary when training large custom models on massive datasets where single-GPU training would take weeks or months, when fine-tuning very large foundation models, or when you need to iterate quickly on large-scale experiments. If your model fits in a single GPU memory and trains in a reasonable timeframe, a cluster adds cost without proportional benefit.
More Questions
For most organisations in Southeast Asia, cloud GPU clusters are the better choice. Building an on-premise cluster requires $500,000 or more in hardware investment, specialised data centre space with high-power cooling, and dedicated staff to manage the infrastructure. Cloud clusters eliminate all of this, providing instant access with per-hour pricing. On-premise clusters only become cost-effective for organisations with sustained, high utilisation over two to three years. Start with cloud, understand your actual needs, and consider on-premise only if utilisation consistently exceeds 60-70%.
Need help implementing GPU Cluster?
Pertama Partners helps businesses across Southeast Asia adopt AI strategically. Let's discuss how gpu cluster fits into your AI roadmap.