Specifications Compared
| Spec | H100 | QUADRO-RTX-5000 |
|---|---|---|
| TDP | 700W | 230W |
| VRAM | 80-94 GB | 16 GB |
| CUDA Cores | 16,896 | 3,072 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Turing |
| Form Factors | SXM5, PCIe, 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 | 3,350 GB/s | 448 GB/s |
Performance Analysis
The H100's FP16 performance of 1979 TFLOPS enables rapid AI model training, processing tensor operations over 176 times faster than the Quadro RTX 5000's 11.2 TFLOPS. For inference, FP8 at 3958 TFLOPS on the H100 supports ultra-low latency serving of large language models, while the Quadro lacks such precision and speed. FP32 at 67 TFLOPS versus 11.2 TFLOPS benefits simulation tasks, reducing iteration times significantly.
Memory bandwidth defines workload scalability: 3350 GB/s on the H100 permits massive batch sizes in training, accommodating models with billions of parameters without swapping. The Quadro's 448 GB/s limits it to smaller datasets, causing bottlenecks in memory-intensive inference. This disparity affects real-world throughput, where H100 sustains higher utilization in distributed setups via NVLink and PCIe 5.0.
Power draw reveals efficiency contexts: the H100's 700W TDP demands robust cooling, yet delivers 8.6 times the FP16 per watt over the Quadro's 230W. For edge cases, Quadro suffices in power-constrained environments, but H100 dominates sustained high-load scenarios.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100 SXM5
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Hyperstack | 4×NVIDIA H100 PCIe 80GB VRAM | 80GB | 124 vCPU 720GB RAM 3300GB Storage | Canada | $1.90/GPU/hr $7.60/hr total (4×) | Available | ||
Hyperstack | 2×NVIDIA H100 PCIe 80GB VRAM | 80GB | 60 vCPU 360GB RAM 1600GB Storage | Canada | $1.90/GPU/hr $3.80/hr total (2×) | Available | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.90/GPU/hr $15.20/hr total (8×) | Available | ||
Hyperstack | NVIDIA H100 PCIe 80GB VRAM | 80GB | 28 vCPU 180GB RAM 850GB Storage | Canada | $1.90/GPU/hr | Available | ||
 Voltage Park | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 928GB RAM 19200GB Storage | Dallas, Texas | $1.99/GPU/hr $15.92/hr total (8×) |
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 H100 SXM5
Opt for the H100 SXM5 in AI training pipelines handling large datasets, where 80 to 94 GB HBM3 VRAM and 3350 GB/s bandwidth enable batch sizes infeasible on 16 GB GDDR6 systems. Its 1979 TFLOPS FP16 accelerates convergence in deep learning, ideal for LLM development or scientific simulations requiring FP32 at 67 TFLOPS.
Cloud deployments benefit from H100's NVLink and InfiniBand for multi-GPU scaling, justifying average $3.54 per hour costs when performance yields faster ROI.
When to Choose the Quadro RTX 5000
Select the Quadro RTX 5000 for professional visualization or CAD workflows, where 16 GB GDDR6 and 11.2 TFLOPS FP32 suffice without data center overhead. Its 230W TDP fits edge servers or laptops, avoiding the H100's 700W demands.
At $0.82 per hour average, it excels in cost-sensitive tasks like rendering or light ML inference, leveraging PCIe form factor for easy integration.
Use Cases
H100's 1979 TFLOPS FP16 and 80-94 GB HBM3 VRAM handle massive parameter sets with large batches. Quadro RTX 5000's 16 GB limits scale.
3958 TFLOPS FP8 on H100 enables low-latency serving at scale. Quadro's 11.2 TFLOPS FP16 cannot match throughput demands.
67 TFLOPS FP32 and 3350 GB/s bandwidth accelerate iterations on H100. Quadro struggles with memory for even mid-sized models.
H100's VRAM supports high-resolution generations without paging. Quadro's 448 GB/s bandwidth bottlenecks complex prompts.
H100's interconnects and 67 TFLOPS FP32 excel in simulations. Quadro fits trivial cases but lacks distributed prowess.
Frequently Asked Questions
Which GPU has more VRAM?▾
The H100 provides 80 to 94 GB HBM3 VRAM, far exceeding the Quadro RTX 5000's 16 GB GDDR6. This enables larger models on H100. Bandwidth follows suit at 3350 GB/s versus 448 GB/s.
What is the FP16 performance difference?▾
H100 achieves 1979 TFLOPS FP16, over 176 times the Quadro RTX 5000's 11.2 TFLOPS. This gap defines AI training speed. FP8 on H100 reaches 3958 TFLOPS, unavailable on Quadro.
How do cloud prices compare?▾
H100 SXM5 starts at $0.80 per hour, averaging $3.54 across 32 offers. Quadro RTX 5000 is $0.82 per hour average across 2 offers. H100 suits high-value tasks.
What are the power requirements?▾
H100 demands 700W TDP, requiring data center infrastructure. Quadro RTX 5000 uses 230W, suitable for workstations. Efficiency favors H100 at 2.8 TFLOPS per watt FP16.
Which is better for multi-GPU setups?▾
H100 supports NVLink, PCIe 5.0, and InfiniBand for scaling. Quadro RTX 5000 offers basic NVLink via PCIe. H100 excels in clusters.
When was each GPU released?▾
H100 uses 2022 Hopper architecture. Quadro RTX 5000 is from 2018 Turing generation. The four-year gap explains spec disparities.
Which is cheaper to rent, the H100 or the Quadro RTX 5000?▾
Cloud rental prices for both the H100 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 H100 have compared to the Quadro RTX 5000?▾
The H100 has 80 to 94 GB of HBM3 memory. The Quadro RTX 5000 has 16 GB of GDDR6 memory.
Can I find H100 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 H100 and the Quadro RTX 5000?▾
The H100 uses the Hopper architecture (2022) while the Quadro RTX 5000 uses Turing (2018). The H100 delivers 176.7x the FP16 throughput and 7.5x the memory bandwidth of the Quadro RTX 5000.