Selecting the right GPU for AI work depends on matching hardware to your specific workload rather than chasing specs. VRAM capacity is the most critical factor—12GB is entry-level, 16GB handles most work, and 24GB+ provides comfort for larger models. Training requires serious memory and bandwidth, while inference needs less. Consumer cards like RTX 4090 make sense to buy for constant use, while data center GPUs (A100, H100, B200) are only available through cloud rental. Start by renting to test different configurations, then buy hardware only when sustained workloads justify the upfront cost. Avoid common mistakes like choosing based on gaming benchmarks, skimping on VRAM, or buying the latest hardware when last-gen options deliver similar performance at lower cost.
