Infrastructure · Fast-moving · Beginner

GPU for AI

Graphics processors whose massively parallel arithmetic makes them the standard hardware for training and serving neural networks.

What GPU for AI is

Neural networks are dominated by matrix multiplication, which maps almost perfectly onto GPU architecture. Memory bandwidth and on-board memory size usually matter more than raw arithmetic throughput.

How it works

Training distributes work across many GPUs connected by high-speed interconnects, with data, tensor and pipeline parallelism. Serving optimises batching and memory to maximise tokens per second per device.

Why it matters

GPU availability and cost set the pace of the entire industry and are a strategic and geopolitical concern, not just a procurement one.

Common uses

→Model pretraining clusters
→Inference serving
→Local model running on workstations

Strengths

✓Orders of magnitude faster than CPUs for this workload
✓Mature software ecosystem

Watch for

✓Expensive and supply-constrained
✓High power and cooling demand

Continue exploring

AI Compute Inference Pretraining Quantization