Specifications Compared
| Spec | H100 | QUADRO-RTX-6000 |
|---|---|---|
| TDP | 700W | 260W |
| VRAM | 80-94 GB | 24 GB |
| CUDA Cores | 16,896 | 4,608 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Turing |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | NVLink |
| Tensor Cores | 528 | 576 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 16.3 TFLOPS |
| FP32 Performance | 67 TFLOPS | 16.3 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 3,350 GB/s | 672 GB/s |
Performance Analysis
The compute disparity shapes real-world applications: H100's 1979 TFLOPS FP16 performance accelerates deep learning training by over 120 times compared to Quadro RTX 6000's 16.3 TFLOPS, enabling faster iterations on large neural networks. FP32 rates of 67 TFLOPS on H100 versus 16.3 TFLOPS on Quadro further favor H100 for general-purpose simulations requiring single-precision accuracy.
For inference, H100's FP8 capability at 3958 TFLOPS supports ultra-efficient low-precision deployments, ideal for serving large language models at scale. The memory bandwidth gap proves critical: H100's 3350 GB/s sustains massive batch sizes in training, preventing bottlenecks with models exceeding 24 GB, while Quadro's 672 GB/s restricts it to smaller datasets.
Interconnects underscore scalability: H100 leverages NVLink, PCIe 5.0, and InfiniBand for multi-GPU clusters, contrasting Quadro's basic NVLink and PCIe support.
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×) |
When to Choose the H100 SXM5
Select the H100 SXM5 for AI training and inference workloads demanding high throughput. Its 1979 TFLOPS FP16 and 80 to 94 GB HBM3 handle large language models and fine-tuning at scales unattainable by older cards. Cloud availability from $0.80 per hour suits bursty, high-compute needs without upfront hardware costs.
When to Choose the Quadro RTX 6000
Opt for the Quadro RTX 6000 in power-constrained workstation environments like CAD or 3D rendering. Its 260W TDP and PCIe form factor integrate easily into desktops, delivering 16.3 TFLOPS FP16 for professional visualization without data center infrastructure. Legacy software compatibility favors it where upgrades prove unnecessary.
Use Cases
H100's 1979 TFLOPS FP16 and 80 to 94 GB HBM3 enable training massive models with large batch sizes. Quadro RTX 6000's 16.3 TFLOPS and 24 GB VRAM cannot scale similarly.
H100's 3958 TFLOPS FP8 supports high-throughput low-precision serving. Quadro lacks FP8 and sufficient bandwidth at 672 GB/s.
H100's 3350 GB/s bandwidth handles parameter-efficient fine-tuning on large models. Quadro's 24 GB limits it to smaller tasks.
H100 accelerates diffusion model generation with 1979 TFLOPS FP16 for faster iterations. Quadro's lower specs prolong rendering times.
H100's 67 TFLOPS FP32 outperforms Quadro's 16.3 TFLOPS for simulations. Its higher VRAM supports complex datasets.
Frequently Asked Questions
What is the VRAM difference between H100 SXM5 and Quadro RTX 6000?▾
H100 SXM5 offers 80 to 94 GB HBM3, compared to Quadro RTX 6000's 24 GB GDDR6. This enables H100 to process much larger models. Bandwidth reaches 3350 GB/s on H100 versus 672 GB/s on Quadro.
How does FP16 performance compare?▾
H100 achieves 1979 TFLOPS FP16, over 120 times the Quadro RTX 6000's 16.3 TFLOPS. This gap accelerates AI training significantly. H100 also adds 3958 TFLOPS FP8 for inference.
What are the power requirements?▾
H100 SXM5 consumes 700W TDP, suited for data centers. Quadro RTX 6000 uses 260W, ideal for workstations. Form factors differ: SXM5, PCIe, NVL for H100 versus PCIe only for Quadro.
Is cloud pricing available for these GPUs?▾
H100 SXM5 starts at $0.80 per hour, averaging $3.54 per hour across 32 offers. No live offers exist for Quadro RTX 6000. This makes H100 viable for on-demand use.
Which has better interconnects?▾
H100 supports NVLink, PCIe 5.0, and InfiniBand for clustering. Quadro RTX 6000 offers NVLink and PCIe. H100 scales better in multi-GPU setups.
When was each GPU released?▾
H100 uses Hopper architecture from 2022. Quadro RTX 6000 relies on Turing from 2018. The four-year gap explains performance differences like 67 TFLOPS FP32 on H100 versus 16.3 TFLOPS.
Which is cheaper to rent, the H100 or the Quadro RTX 6000?▾
Cloud rental prices for both the H100 and Quadro RTX 6000 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 6000?▾
The H100 has 80 to 94 GB of HBM3 memory. The Quadro RTX 6000 has 24 GB of GDDR6 memory.
Can I find H100 and Quadro RTX 6000 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 6000?▾
The H100 uses the Hopper architecture (2022) while the Quadro RTX 6000 uses Turing (2018). The H100 delivers 121.4x the FP16 throughput and 5.0x the memory bandwidth of the Quadro RTX 6000.