GPU Workload Types — Training vs Inference vs Rendering

Why it matters

Wrong GPU for workload wastes. Understanding shapes right-sized deployment.

The architecture

Training: throughput + memory.

Inference: latency + throughput.

Rendering: real-time + FP32.

HPC: FP64.

How it works end to end

Training needs: max throughput, huge memory (80GB+), fast interconnect.

Inference: latency-optimized, smaller memory OK, MIG for multi-tenancy.

Graphics: FP32 shaders, DisplayPort output.

HPC: FP64 (rare in AI-focused GPUs).