Specifications Compared
| Spec | H200 | QUADRO-RTX-5000 |
|---|---|---|
| TDP | 700W | 230W |
| VRAM | 141 GB | 16 GB |
| CUDA Cores | 16,896 | 3,072 |
| Memory Type | HBM3e | GDDR6 |
| Architecture | Hopper | Turing |
| Form Factors | SXM, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | NVLink |
| Tensor Cores | 528 | 384 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 11.2 TFLOPS |
| FP32 Performance | 67 TFLOPS | 11.2 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 4,800 GB/s | 448 GB/s |
Performance Analysis
FP16 performance defines training efficiency: the H200 SXM's 1979 TFLOPS enables processing massive neural networks in hours that take days on the Quadro RTX 5000's 11.2 TFLOPS. FP32 at 67 TFLOPS on H200 SXM accelerates simulations versus the Quadro's 11.2 TFLOPS, where single precision matters for accuracy in engineering. The H200 SXM's FP8 capability of 3958 TFLOPS further boosts inference throughput for quantized models. Memory bandwidth of 4800 GB/s on H200 SXM supports batch sizes up to hundreds in LLM training, minimizing overhead and epochs, while 448 GB/s on Quadro RTX 5000 restricts to small batches prone to underutilization. TDP difference, 700W versus 230W, implies H200 SXM suits high density clusters, though power costs rise proportionally.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H200 SXM
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vultr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 960GB Storage | Atlanta | $1.99/GPU/hr | Available | ||
 Lambda Labs | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 64 vCPU 432GB RAM 4096GB Storage | Virginia | $2.29/GPU/hr | Available | ||
 Nebius | NVIDIA H200 SXM 141GB VRAM | 141GB | 16 vCPU 200GB RAM | 🌍Europe | $2.45/GPU/hr | |||
 CoreWeave | 8×NVIDIA H200 SXM 141GB VRAM | 141GB | 128 vCPU 0GB RAM 61440GB Storage | United States | $2.58/GPU/hr $20.64/hr total (8×) | |||
 Ori | 2×NVIDIA H200 SXM 141GB VRAM | 141GB | 48 vCPU 480GB RAM 6000GB Storage | London | $3.50/GPU/hr $7.00/hr total (2×) | Available |
Quadro RTX 5000
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Paperspace | NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 8 vCPU 30GB RAM 50GB Storage | New York | $0.82/GPU/hr | Available | ||
Paperspace | 2×NVIDIA Quadro RTX 5000 16GB VRAM | 16GB | 16 vCPU 60GB RAM 50GB Storage | New York | $0.82/GPU/hr $1.64/hr total (2×) | Available |
When to Choose the H200 SXM
Choose the NVIDIA H200 SXM for large scale AI training and inference where 141 GB HBM3e VRAM handles models exceeding 100 billion parameters without partitioning. Its 1979 TFLOPS FP16 and 4800 GB/s bandwidth excel in distributed setups via NVLink, PCIe 5.0, and InfiniBand. Cloud users prioritize it at $1.19 per hour starting price for workloads demanding peak throughput.
When to Choose the Quadro RTX 5000
Opt for the NVIDIA Quadro RTX 5000 in budget constrained visualization or CAD tasks fitting within 16 GB GDDR6. Its 230W TDP and PCIe form factor integrate easily into workstations without data center cooling. At $0.82 per hour, it delivers value for legacy software not leveraging Hopper features.
Use Cases
H200 SXM's 141 GB VRAM and 1979 TFLOPS FP16 support training models over 100B parameters with large batches. Quadro RTX 5000's 16 GB limits to tiny models.
3958 TFLOPS FP8 and 4800 GB/s bandwidth on H200 SXM serve high concurrency. Quadro RTX 5000's 11.2 TFLOPS FP16 bottlenecks large scale deployment.
67 TFLOPS FP32 and vast VRAM enable efficient fine-tuning on full datasets. Quadro RTX 5000 struggles with memory at 16 GB.
H200 SXM's bandwidth handles high resolution generations rapidly. Quadro RTX 5000 suffices for basic use but slows at scale.
H200 SXM's 67 TFLOPS FP32 outperforms Quadro's 11.2 TFLOPS for simulations. Interconnects enhance multi-GPU scaling.
Frequently Asked Questions
What is the VRAM difference between NVIDIA H200 SXM and Quadro RTX 5000?▾
NVIDIA H200 SXM has 141 GB HBM3e VRAM. Quadro RTX 5000 offers 16 GB GDDR6. This allows H200 SXM to manage datasets nine times larger.
How do FP16 performances compare?▾
H200 SXM achieves 1979 TFLOPS FP16. Quadro RTX 5000 reaches 11.2 TFLOPS. The gap accelerates AI training significantly.
What are the cloud rental prices?▾
H200 SXM starts from $1.19 per hour, averaging $3.71 per hour across 22 offers. Quadro RTX 5000 is $0.82 per hour average across 2 offers.
Which has higher memory bandwidth?▾
H200 SXM provides 4800 GB/s. Quadro RTX 5000 has 448 GB/s. This impacts batch sizes in deep learning.
What architectures do they use?▾
H200 SXM uses Hopper from 2024. Quadro RTX 5000 employs Turing from 2018. Hopper optimizes for modern AI.
Compare their TDPs?▾
H200 SXM TDP is 700W. Quadro RTX 5000 TDP is 230W. Higher TDP correlates with compute density.
Which is cheaper to rent, the H200 or the Quadro RTX 5000?▾
Cloud rental prices for both the H200 and Quadro RTX 5000 vary by provider, configuration, and availability. This page shows live pricing from 25+ providers updated every 60 seconds. Scroll to the Live Cloud Pricing section to compare current rates.
How much VRAM does the H200 have compared to the Quadro RTX 5000?▾
The H200 has 141 GB of HBM3e memory. The Quadro RTX 5000 has 16 GB of GDDR6 memory.
Can I find H200 and Quadro RTX 5000 GPUs available to rent right now?▾
Yes. This page shows real-time availability across 25+ cloud GPU providers. The Live Cloud Pricing section displays only in-stock offers with current pricing.
What is the main difference between the H200 and the Quadro RTX 5000?▾
The H200 uses the Hopper architecture (2024) while the Quadro RTX 5000 uses Turing (2018). The H200 delivers 176.7x the FP16 throughput and 10.7x the memory bandwidth of the Quadro RTX 5000.