NVIDIA GPU Comparison for AI Workloads (2026)

Consumer GPUs (GeForce RTX)

| GPU | VRAM | FP16 TFLOPS | Best For | Price |

|-----|------|-------------|----------|-------|

| RTX 4060 Ti 16GB | 16GB GDDR6 | 22.1 | Learning, small models | ~$400 |

| RTX 4070 Ti Super | 16GB GDDR6X | 44.1 | Fine-tuning 7B models | ~$800 |

| RTX 4090 | 24GB GDDR6X | 82.6 | Fine-tuning, local inference | ~$1,600 |

| RTX 5090 | 32GB GDDR7 | 104.8 | Advanced local AI | ~$2,000 |

Data Center GPUs

| GPU | VRAM | FP8 TFLOPS | Architecture | Price |

|-----|------|------------|--------------|-------|

| A100 80GB | 80GB HBM2e | 624 | Ampere | ~$10,000 |

| H100 SXM | 80GB HBM3 | 1,979 | Hopper | ~$25,000 |

| H200 | 141GB HBM3e | 1,979 | Hopper | ~$30,000 |

| B200 | 192GB HBM3e | 4,500 | Blackwell | ~$35,000 |

| GB200 NVL72 | 13.5TB total | 720 PFLOPS FP4 | Grace Blackwell | Rack system |

Key Considerations

**VRAM is king for AI.** The single most important spec for LLM work is GPU memory:

- 7B parameter model: needs ~14GB (FP16) or ~7GB (INT8)

- 13B parameter model: needs ~26GB (FP16)

- 70B parameter model: needs ~140GB (FP16) — requires multi-GPU or H200

**Interconnect matters for training.** NVLink bandwidth determines multi-GPU scaling:

- RTX 4090: No NVLink (PCIe only)

- H100: 900 GB/s NVLink

- B200: 1,800 GB/s NVLink 5th gen

Cloud GPU Pricing (Approximate)

| Provider | H100 (per hour) | A100 (per hour) |

|----------|-----------------|------------------|

| AWS (p5) | $32.77 | $19.50 |

| GCP (a3-highgpu) | $31.22 | $19.82 |

| Azure (ND H100) | $33.50 | $20.00 |

| Lambda Labs | $2.49 | $1.29 |

| RunPod | $3.89 | $1.64 |