GPU computing has revolutionized everything from gaming to artificial intelligence. While CPUs have 8-64 cores for sequential tasks, GPUs have thousands of smaller cores designed for parallel computation — making them 10-100x faster for the right workloads.
GPU vs CPU: Key Difference
CPU (Central Processing Unit)
8–64 powerful cores. High clock speeds (up to 5.8 GHz). Excellent for sequential tasks. General-purpose computing.
GPU (Graphics Processing Unit)
Thousands of simpler cores. Lower clock speeds (1–2 GHz). Designed for parallel tasks. Optimized for throughput over latency.
Key GPU Architecture Components
- CUDA Cores (NVIDIA) / Stream Processors (AMD): Basic computational units. More cores = more parallel processing power.
- Tensor Cores: Specialized AI/ML cores — 8-12x faster for neural network training than CUDA cores alone.
- RT Cores: Dedicated ray tracing hardware for realistic lighting in games and 3D rendering.
- VRAM: GPU's dedicated memory. Critical for AI/ML — unlike system RAM, it's much faster and on the GPU die itself.
2026 GPU Comparison
| GPU Model | VRAM | Bandwidth | Best For |
|---|---|---|---|
| RTX 4090 | 24GB GDDR6X | 1,008 GB/s | Gaming, Consumer AI, Fine-tuning |
| A100 (40GB) | 40GB HBM2e | 1,555 GB/s | Professional AI/ML training |
| H100 | 80GB HBM3 | 3,350 GB/s | Large language model training |
| H200 | 141GB HBM3e | 4,800 GB/s | Frontier AI research |
Choosing the Right GPU for Your Use Case
| Use Case | Recommended GPU | Min. VRAM | Budget |
|---|---|---|---|
| 4K Gaming | RTX 4080 / RX 7900 XTX | 16GB | £700–1,000 |
| AI Fine-tuning (hobby) | RTX 4090 | 24GB | £1,600 |
| Professional ML | A100 (cloud rental) | 40–80GB | £2–4/hour cloud |
| Large Language Models | H100 cluster | 80GB×8+ | £15,000+/GPU |
| 3D Rendering | RTX 4090 / A6000 | 24–48GB | £1,600–4,000 |
Cloud vs buying: For most people, cloud GPUs (AWS, Google Cloud, Vast.ai) are better value. If you use a GPU less than 4 hours/day, cloud rental at £2-8/hour is cheaper than buying a £1,600 RTX 4090.
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