H100 SXM vs RTX 6000 Ada
Detailed comparison of specifications, performance, and pricing between NVIDIA H100 SXM and NVIDIA RTX 6000 Ada
Difference Analysis
Full Specifications
| Specification | H100 SXM | RTX 6000 Ada | AMD Instinct MI100 |
|---|---|---|---|
| Brand | NVIDIA | NVIDIA | AMD |
| Series | Data Center | Workstation | Data Center |
| Architecture | Hopper | Ada Lovelace | CDNA |
| VRAM | 80GB | 48GB | 32GB |
| VRAM Type | HBM3 | GDDR6 | HBM2 |
| Memory Bandwidth | 3.4 TB/s | 960 GB/s | 1.2 TB/s |
| FP16 TFLOPS | 134.0 | 182.2 | 184.6 |
| Tensor TFLOPS | 2.0k | - | 184.6 |
| TDP | 700W | 300W | 300W |
| Form Factor | SXM | PCIe | - |
| Hardware Price | $$32k | $$7.0k | - |
| Cloud Price (min) | $2.10/hr | $0.750/hr | - |
Which Should You Choose?
For AI Training
Large model training needs maximum VRAM and memory bandwidth.
For AI Inference
Inference prioritizes throughput and cost efficiency.
For Cloud Rental
Minimize hourly costs for cloud workloads.
Related Comparisons
H100 SXM vs RTX 6000 Ada FAQ
It depends on your use case. The H100 SXM offers 986% better performance (2.0k vs 182.2 TFLOPS). However, the RTX 6000 Ada is 357% cheaper. For raw performance, choose H100 SXM. For value, consider your budget and workload requirements.
The H100 SXM has more VRAM with 80GB compared to 48GB (67% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.
For AI training, the H100 SXM 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 48GB, the cheaper option may be more cost-effective.
The RTX 6000 Ada is 357% cheaper at $$7.0k vs $$32k. When considering performance per dollar, evaluate your specific workload requirements to determine the best value.
Upgrading to H100 SXM would give you 986% more performance and 67% more VRAM. The upgrade cost difference is approximately $$25k. Consider if your workloads are bottlenecked by current GPU capabilities.