Lilly Tech Systems
Introduction to GPU Cloud Computing Beginner
Graphics Processing Units have become the engine of the AI revolution. Originally designed for rendering pixels, GPUs excel at the massively parallel matrix operations that underpin deep learning. This lesson explains why GPUs dominate AI computing and introduces the GPU cloud landscape.
Why GPUs for AI?
The fundamental difference between CPUs and GPUs is parallelism:
| Feature | CPU | GPU |
|---|---|---|
| Cores | 8-128 high-power cores | Thousands of smaller cores |
| Parallelism | Task-level (few threads) | Data-level (thousands of threads) |
| Memory Bandwidth | ~100 GB/s | ~3,000 GB/s (H100) |
| AI Operations | ~1 TFLOPS FP16 | ~1,000 TFLOPS FP16 (H100) |
| Best For | Sequential logic, branching | Matrix math, parallel data processing |
Key Insight: Neural network training is dominated by matrix multiplications. A single forward pass through a transformer layer involves multiplying matrices with millions of elements — exactly the kind of work GPUs excel at.
The GPU Cloud Landscape
Every major cloud provider now offers GPU instances, and new GPU cloud providers have emerged to meet AI demand:
- Hyperscalers — AWS, GCP, Azure offer the broadest GPU selection with integrated ecosystems
- GPU cloud specialists — CoreWeave, Lambda Cloud, and RunPod focus exclusively on GPU compute
- GPU marketplaces — Vast.ai and FluidStack aggregate GPU capacity from multiple providers
GPU Generations
Understanding GPU generations helps in selecting the right hardware:
- Volta (2017) — V100, first Tensor Cores, 16GB/32GB HBM2
- Ampere (2020) — A100, 3rd gen Tensor Cores, 40GB/80GB HBM2e, MIG support
- Ada Lovelace (2022) — L4/L40, optimized for inference, DLSS/video AI
- Hopper (2022) — H100, Transformer Engine, 80GB HBM3, NVLink 4.0
- Blackwell (2024) — B100/B200, 2nd gen Transformer Engine, HBM3e
Ready to Explore NVIDIA GPUs?
The next lesson provides a deep technical dive into NVIDIA GPU architectures for AI workloads.
Next: NVIDIA GPUs →