H100 PCIe vs RTX 5090

Detailed comparison of specifications, performance, and pricing between NVIDIA H100 PCIe and RTX 5090

🏆
Overall Winner
H100 PCIe
Wins 4 of 7 categories
Performance Leader
H100 PCIe
1.5k TFLOPS (+0%)

Difference Analysis

Metric
H100 PCIe
Difference
RTX 5090
Tensor TFLOPS
1.5k
=
-
VRAM
80GB
+150%
32GB
Memory Bandwidth
2.0 TB/s
=
-
Hardware Price
$$28k
=
-
Cloud Price/hr
$2.39
+169%
$0.890

Full Specifications

Specification H100 PCIe RTX 5090
Brand NVIDIA NVIDIA
Series Data Center -
Architecture Hopper -
VRAM 80GB 32GB
VRAM Type HBM2e -
Memory Bandwidth 2.0 TB/s -
FP16 TFLOPS 102.0 -
Tensor TFLOPS 1.5k -
TDP 350W -
Form Factor PCIe -
Hardware Price $$28k -
Cloud Price (min) $2.39/hr $0.890/hr

Which Should You Choose?

🧠

For AI Training

Large model training needs maximum VRAM and memory bandwidth.

Recommended: H100 PCIe
80GB VRAM · 2.0 TB/s

For AI Inference

Inference prioritizes throughput and cost efficiency.

Recommended: H100 PCIe
Best performance per dollar
☁️

For Cloud Rental

Minimize hourly costs for cloud workloads.

Recommended: RTX 5090
From $0.890/hr

H100 PCIe vs RTX 5090 FAQ

It depends on your use case. The H100 PCIe offers 0% better performance (1.5k vs - TFLOPS). For raw performance, choose H100 PCIe. For value, consider your budget and workload requirements.

The H100 PCIe has more VRAM with 80GB compared to 32GB (150% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.

For AI training, the H100 PCIe is generally better due to its larger VRAM (80GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 32GB, the cheaper option may be more cost-effective.

Price comparison requires both GPUs to have available pricing data. Check individual GPU pages for current market prices.

Upgrading to H100 PCIe would give you 0% more performance and 150% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.