What is GPU?
A GPU, or Graphics Processing Unit, is a specialised processor originally designed for rendering graphics but now essential for AI and machine learning workloads, capable of performing thousands of calculations simultaneously, making it far more efficient than traditional CPUs for training and running AI models.
What Is a GPU?
A GPU, or Graphics Processing Unit, is a specialised computer chip designed to perform many calculations in parallel. While a traditional CPU (Central Processing Unit) excels at handling complex sequential tasks, a GPU can process thousands of simpler operations simultaneously. This parallel processing capability, originally developed for rendering video game graphics, has made GPUs the backbone of modern artificial intelligence.
Training an AI model involves performing billions of mathematical operations on large datasets. A task that might take weeks on a CPU can be completed in hours or even minutes on a GPU. This is why GPUs have become the most critical hardware component in the AI revolution.
How GPUs Power AI
The reason GPUs are so effective for AI lies in the nature of machine learning calculations. AI models, particularly deep learning neural networks, rely heavily on matrix multiplication, a type of mathematical operation that is highly parallelisable. Consider the difference:
- A modern CPU has 8-64 powerful cores that handle complex tasks sequentially
- A modern GPU has thousands of smaller cores that handle simpler tasks simultaneously
When training a large language model or processing images through a neural network, the workload can be split across thousands of GPU cores working in parallel. This is why companies like NVIDIA have become some of the most valuable in the world, as their GPUs power the AI infrastructure that companies worldwide depend on.
The most common GPUs used for AI include:
- NVIDIA A100 and H100: Enterprise-grade GPUs designed specifically for AI workloads, commonly available through cloud providers
- NVIDIA T4: A cost-effective option popular for inference workloads
- NVIDIA RTX series: Professional GPUs that can handle moderate AI workloads
- AMD Instinct series: An emerging alternative to NVIDIA for data centre AI
Why GPUs Matter for Business
For most SMBs in Southeast Asia, the relevance of GPUs is not about buying physical hardware but about understanding how GPU access affects your AI strategy and costs:
- Cloud GPU costs are the primary expense for AI projects. Understanding GPU pricing helps you budget accurately and avoid overspending.
- GPU availability can be a bottleneck. During periods of high demand, cloud GPU instances can be scarce and expensive, particularly in ASEAN cloud regions.
- Choosing the right GPU for your workload can mean the difference between an affordable AI project and a prohibitively expensive one.
GPU Access for SMBs in Southeast Asia
Most businesses do not need to purchase GPUs directly. Instead, they access GPU computing power through cloud providers. The major options available in the ASEAN region include:
- AWS (Singapore, Jakarta): Offers a wide range of GPU instances including P4d (A100) and G4dn (T4) types
- Google Cloud (Singapore, Jakarta): Provides A100 and T4 GPU instances with strong AI/ML platform integration
- Azure (Singapore, Kuala Lumpur): Offers NVIDIA A100 and T4 instances with integration into Microsoft AI services
- Regional providers: Companies like Alibaba Cloud have data centres in Singapore, Jakarta, and Kuala Lumpur with competitive GPU pricing
For businesses that need dedicated GPU hardware, co-location facilities in Singapore offer a middle ground between cloud and on-premise ownership.
Practical GPU Strategies
Understanding GPU economics is essential for managing AI project costs effectively:
- Right-size your GPU selection. Do not default to the most powerful GPU. Many inference workloads run efficiently on T4 instances that cost a fraction of A100 pricing.
- Use spot or preemptible instances for training workloads that can tolerate interruptions. These can reduce GPU costs by 60-80%.
- Optimise your models to require fewer GPU resources. Techniques like quantisation and pruning can reduce inference costs dramatically.
- Schedule GPU usage to avoid paying for idle resources. Training jobs can run during off-peak hours when spot prices are lower.
- Consider GPU-as-a-Service platforms like Lambda Labs or CoreWeave that specialise in GPU access for AI workloads and may offer better pricing than general cloud providers.
The GPU landscape is evolving rapidly, with new chips from NVIDIA, AMD, and custom silicon from Google (TPUs) and Amazon (Inferentia/Trainium) constantly changing the cost-performance equation. Staying informed about these developments is important for making cost-effective AI infrastructure decisions.
GPUs are the engine behind every AI system, and understanding them is essential for any leader investing in artificial intelligence. The cost of GPU computing is typically the largest line item in AI project budgets, and making informed decisions about GPU selection and usage can mean the difference between an AI initiative that delivers strong ROI and one that burns through budget before delivering value.
For CTOs and technical leaders, GPU strategy directly impacts your ability to build, train, and deploy AI systems. The current global demand for AI GPUs has created supply constraints that affect cloud availability and pricing, particularly in ASEAN cloud regions. Planning your GPU needs in advance and establishing relationships with cloud providers can prevent costly delays in your AI roadmap.
For CEOs, the GPU question is fundamentally about cost management and competitive positioning. Companies that optimise their GPU usage, by choosing the right hardware for each workload, leveraging spot pricing, and efficiently scheduling resources, can achieve the same AI capabilities as larger competitors at a fraction of the cost. This is particularly relevant for SMBs in Southeast Asia that need to compete with better-funded multinationals.
- Do not purchase physical GPUs unless you have consistent, high-volume AI workloads. Cloud GPU access is more flexible and cost-effective for most SMBs.
- Match your GPU selection to your workload. Use powerful A100/H100 GPUs for training and cost-effective T4 GPUs for inference to optimise your spending.
- Monitor GPU utilisation closely. Paying for GPU instances that sit idle is one of the most common sources of wasted AI spending.
- Take advantage of spot and preemptible GPU instances for training workloads. The savings of 60-80% can make previously unaffordable projects viable.
- Plan for GPU availability in your ASEAN cloud region. High-demand GPU types can have waitlists, so reserve capacity in advance for critical projects.
- Explore model optimisation techniques like quantisation and distillation that can reduce GPU requirements by 50% or more without significantly impacting performance.
- Stay informed about alternative AI chips such as Google TPUs and AWS Inferentia, which may offer better price-performance for specific workloads.
Frequently Asked Questions
Why are GPUs used for AI instead of regular CPUs?
GPUs can perform thousands of calculations simultaneously through parallel processing, while CPUs handle tasks mostly in sequence. AI workloads, particularly deep learning, involve massive amounts of matrix mathematics that are perfectly suited to parallel execution. A task that takes a CPU several weeks can be completed by a GPU in hours. This massive speed advantage makes GPUs essential for training AI models and running them at scale.
How much does GPU cloud computing cost in Southeast Asia?
GPU cloud costs in ASEAN regions vary by provider and GPU type. An NVIDIA T4 instance suitable for inference typically costs $0.50-1.50 USD per hour. More powerful A100 instances for training can cost $3-5 USD per hour. Monthly costs for a typical SMB AI project range from $500-5,000 USD depending on workload intensity. Using spot instances and optimising model efficiency can reduce these costs by 50-80%.
More Questions
Not necessarily. Many AI services, such as ChatGPT, Google AI APIs, and pre-built SaaS AI tools, handle GPU requirements behind the scenes. You only need to think about GPU access if you are training custom AI models or running inference at significant scale. For most SMBs starting their AI journey, using managed AI services eliminates the need to manage GPU infrastructure directly.
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