How are GPUs Used in LLM Work?
Many executives assume AI runs like regular software on standard servers. In reality, Large Language Models (LLMs) require massive computing power, and traditional CPUs aren’t enough.
The key technology behind AI’s performance? GPUs (Graphics Processing Units)—specialized processors designed for high-speed parallel computations.
GPUs are essential for both training and running AI models because they:
✅ Handle thousands of simultaneous calculations, making AI efficient.
✅ Process matrix operations, which are the foundation of neural networks.
✅ Enable faster model training and inference, reducing operational costs.
✅ Are critical for on-premises AI deployment, affecting hardware investment decisions.
Executives looking to implement AI must consider GPU access, cost, and infrastructure as part of their strategy.
Why CPUs Aren’t Enough for AI
Traditional business software runs on CPUs (Central Processing Units), which are designed for general-purpose computing—handling one or a few tasks at a time.
GPUs, however, are optimized for parallel processing, allowing them to perform thousands of mathematical operations simultaneously—a requirement for training and running LLMs.
| Processor Type | Best For | Limitations for AI |
|---|---|---|
| CPU (Central Processing Unit) | General computing, databases, web servers | Too slow for large-scale AI computations |
| GPU (Graphics Processing Unit) | AI model training & inference, parallel processing | Expensive and energy-intensive |
| TPU (Tensor Processing Unit) | AI acceleration in the cloud | Requires custom integration, limited availability |
💡 AI workloads are fundamentally different from traditional computing—choosing the right hardware is key.
How GPUs Are Used in LLM Work
1️⃣ Training AI Models (Compute-Intensive Phase)
Training an LLM involves processing trillions of words, requiring:
✔️ Massive matrix calculations for adjusting AI’s internal weights.
✔️ Thousands of GPUs running in parallel for weeks or months.
✔️ Cloud-scale clusters from companies like NVIDIA, Google, and Microsoft.
🚨 Why it matters: Training AI in-house requires massive GPU investment, while cloud providers rent GPU power at a premium.
2️⃣ Running AI Models (Inference Phase)
Once trained, an AI model must process real-world inputs—this is called inference.
✔️ Every AI response requires billions of calculations.
✔️ GPUs speed up responses from minutes to milliseconds.
✔️ On-premises AI requires dedicated GPU servers, while cloud AI uses shared resources.
🚨 Why it matters: Businesses deploying AI at scale must balance speed vs. cost when choosing GPUs for inference.
The Cost of AI: GPUs vs. Cloud AI
Executives must weigh buying vs. renting GPU power.
| AI Deployment | Pros | Cons |
|---|---|---|
| Cloud AI (GPT, Claude, Gemini, etc.) | No hardware costs, scales easily | Expensive long-term, no full control over data |
| On-Premises GPUs | Full control, lower costs over time | High upfront investment, requires IT management |
🚨 Key Consideration: Cloud AI is easier but costly, while on-prem AI requires an infrastructure investment.
Final Thoughts
GPUs are the backbone of modern AI, powering both training and inference. Any executive considering AI adoption, local AI models, or enterprise-scale deployment must account for:
✔️ The need for GPUs in AI processing.
✔️ The cost trade-offs between cloud and on-prem AI.
✔️ How AI workloads differ from traditional computing.
Understanding why GPUs are essential is the first step toward making informed AI investment decisions.