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NVIDIA H100 vs A100 vs RTX 4090: Which GPU for AI?

3/5/2026
14 min read

NVIDIA H100 vs A100 vs RTX 4090: Which GPU for AI?

Choosing between the H100, A100, and RTX 4090 is the most common decision AI practitioners face in 2026. Each GPU serves different use cases, and the price differences are significant. This guide covers real-world benchmarks, pricing across providers, and our recommendations for every use case.

Specifications at a Glance

| Specification | H100 80GB SXM | A100 80GB SXM | RTX 4090 |

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

| Architecture | Hopper | Ampere | Ada Lovelace |

| VRAM | 80GB HBM3 | 80GB HBM2e | 24GB GDDR6X |

| Memory Bandwidth | 3.35 TB/s | 2.0 TB/s | 1.0 TB/s |

| FP16 Performance | 989 TFLOPS | 312 TFLOPS | 330 TFLOPS |

| FP8 Performance | 1,979 TFLOPS | N/A | 660 TFLOPS |

| TDP | 700W | 400W | 450W |

| Tensor Cores | 4th Gen | 3rd Gen | 4th Gen |

| NVLink | Yes (900 GB/s) | Yes (600 GB/s) | No |

| Cloud Price (On-Demand) | $2.49-3.89/hr | $1.69-2.49/hr | $0.39-0.89/hr |

Performance Benchmarks

LLM Training Throughput (tokens/second)

| Model | H100 | A100 | RTX 4090 |

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

| GPT-2 (1.5B) | 45,000 | 22,000 | 18,000 |

| LLaMA 3 8B | 12,000 | 5,800 | 4,200 |

| LLaMA 3 70B | 1,800 | 850 | N/A (OOM) |

| Mistral 7B | 13,500 | 6,200 | 4,800 |

Image Generation (Stable Diffusion XL, images/min)

| Resolution | H100 | A100 | RTX 4090 |

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

| 512x512 | 180 | 95 | 72 |

| 1024x1024 | 95 | 48 | 38 |

| 2048x2048 | 32 | 15 | 11 |

LLM Inference (tokens/second, LLaMA 3 8B)

| Batch Size | H100 | A100 | RTX 4090 |

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

| 1 | 120 | 65 | 55 |

| 8 | 680 | 340 | 240 |

| 32 | 2,100 | 980 | 520 |

| 128 | 5,200 | 2,400 | OOM |

Cost Efficiency Analysis

The raw performance numbers only tell half the story. What matters is **performance per dollar**.

Training Cost Efficiency (tokens per dollar)

| GPU | Price/hr | Tokens/sec (8B) | Tokens/$ |

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

| H100 | $2.49 | 12,000 | 17,349,398 |

| A100 | $1.89 | 5,800 | 11,047,619 |

| RTX 4090 | $0.44 | 4,200 | 34,363,636 |

**The RTX 4090 delivers nearly 2x more tokens per dollar than the H100 for training** (when VRAM is not a constraint).

Inference Cost Efficiency (tokens per dollar, batch 8)

| GPU | Price/hr | Tokens/sec | Tokens/$ |

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

| H100 | $2.49 | 680 | 983,133 |

| A100 | $1.89 | 340 | 647,619 |

| RTX 4090 | $0.44 | 240 | 1,963,636 |

When to Choose Each GPU

Choose H100 When:

  • Training models with 70B+ parameters
  • You need NVLink for multi-GPU scaling
  • Running large batch inference (128+ concurrent)
  • Speed to completion matters more than cost
  • Working with FP8 for maximum throughput
  • Choose A100 When:

  • Training 7B-70B models with LoRA
  • Running medium-scale inference services
  • You need 80GB VRAM but not H100 speeds
  • Budget is a significant concern
  • Using established frameworks optimized for Ampere
  • Choose RTX 4090 When:

  • Training models under 13B parameters
  • Running QLoRA fine-tuning on 7B-8B models
  • Personal research and experimentation
  • Image generation (Stable Diffusion, DALL-E)
  • Maximum cost efficiency is the priority
  • You do not need more than 24GB VRAM
  • Provider Pricing Comparison (March 2026)

    H100 80GB SXM

    | Provider | On-Demand | Spot |

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

    | RunPod | $2.49/hr | $1.49/hr |

    | Lambda Labs | $2.49/hr | $1.79/hr |

    | Vast.ai | $2.60/hr | $1.55/hr |

    | Paperspace | $3.09/hr | N/A |

    | AWS (p5.xlarge) | $3.89/hr | $1.56/hr |

    A100 80GB SXM

    | Provider | On-Demand | Spot |

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

    | Vast.ai | $1.69/hr | $0.89/hr |

    | RunPod | $1.89/hr | $1.09/hr |

    | Lambda Labs | $1.99/hr | $1.29/hr |

    | Paperspace | $2.49/hr | N/A |

    | AWS (p4d.xlarge) | $2.79/hr | $1.12/hr |

    RTX 4090

    | Provider | On-Demand | Spot |

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

    | Vast.ai | $0.39/hr | $0.19/hr |

    | RunPod | $0.44/hr | $0.24/hr |

    | Paperspace | $0.69/hr | N/A |

    Our Recommendation Matrix

    | Use Case | Recommended GPU | Monthly Budget |

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

    | Hobby AI projects | RTX 4090 | $20-50 |

    | Startup training | A100 80GB | $200-500 |

    | Production inference | H100 or A100 | $500-2,000 |

    | Enterprise training | Multi-H100 | $2,000+ |

    | Image generation | RTX 4090 | $30-100 |

    | LLM fine-tuning (7-8B) | RTX 4090 | $10-30 |

    | LLM fine-tuning (70B) | A100 80GB | $50-200 |

    The Bottom Line

    There is no single "best" GPU -- it depends entirely on your workload and budget. The **RTX 4090 offers unbeatable cost efficiency** for smaller models and image generation. The **A100 is the versatile workhorse** for most professional AI work. The **H100 is essential** only for the largest models and highest-throughput inference.

    Use BestGPUCloud to compare real-time prices across all providers and find the best deal for your specific GPU needs.

    Start comparing prices now -->

    LF

    Lucas Ferreira

    Senior AI Engineer

    Ex-NVIDIA, spent 3 years benchmarking data center GPUs. Now helps teams pick the right hardware for their ML workloads. Ran inference benchmarks on every GPU generation since Volta.

    GPU BenchmarksInference OptimizationCUDAHardware

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