Specifications Compared
| Spec | H100 | RTX-5070 |
|---|---|---|
| TDP | 700W | 250W |
| VRAM | 80-94 GB | 12 GB |
| CUDA Cores | 16,896 | 6,144 |
| Memory Type | HBM3 | GDDR7 |
| Architecture | Hopper | Blackwell |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 192 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 40.6 TFLOPS |
| FP32 Performance | 67 TFLOPS | 40.6 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | 650 TOPS |
| Memory Bandwidth | 3,350 GB/s | 448 GB/s |
Performance Analysis
The H100's FP16 performance of 1979 TFLOPS vastly outpaces the RTX 5070's 40.6 TFLOPS, accelerating AI training by handling larger datasets faster. Its FP32 rate of 67 TFLOPS exceeds the RTX 5070's 40.6 TFLOPS, benefiting scientific simulations requiring single-precision math. This delta means H100 completes training epochs quicker for large language models.
Memory bandwidth defines batch size capabilities: H100's 3350 GB/s supports massive batches in inference, reducing latency, whereas RTX 5070's 448 GB/s constrains it to smaller models or lower resolutions. For inference, H100's 80 to 94 GB VRAM loads full models without swapping, unlike RTX 5070's 12 GB limit.
Power draw influences deployment: H100's 700W TDP suits enterprise cooling, while RTX 5070's 250W enables consumer setups. Overall, H100 excels in throughput-heavy tasks, RTX 5070 in power-sensitive ones.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100
| 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
Choose the H100 for large-scale AI training and inference where 80 to 94 GB HBM3 VRAM handles models exceeding 12 GB. Its 3350 GB/s bandwidth and 1979 TFLOPS FP16 performance enable efficient processing of billion-parameter LLMs. Cloud users needing NVLink interconnects for multi-GPU scaling prefer it despite $3.14 per hour average cost.
Enterprise teams running FP8 workloads at 3958 TFLOPS select H100 for datacenter reliability across SXM5 and PCIe form factors.
When to Choose the RTX 5070
Opt for the RTX 5070 in budget-conscious gaming or lightweight AI inference with models fitting 12 GB GDDR7 VRAM. Its 40.6 TFLOPS FP16 and FP32 rates suffice for Stable Diffusion at 448 GB/s bandwidth. At $0.17 per hour average, it appeals to individual developers testing prototypes.
Power-limited environments favor its 250W TDP and PCIe form factor for quick cloud spins without high cooling demands.
Use Cases
H100's 1979 TFLOPS FP16 and 80 to 94 GB VRAM support large batch sizes for billion-parameter models. RTX 5070's 12 GB limits scale.
H100's 3350 GB/s bandwidth and high FP8 at 3958 TFLOPS deliver low-latency serving for full models. RTX 5070 suits quantized small models only.
H100 accelerates with 67 TFLOPS FP32 for complex datasets; RTX 5070 works for datasets under 12 GB at lower cost.
RTX 5070's 40.6 TFLOPS FP16 handles image generation efficiently within 12 GB VRAM. H100 overkill for single-user tasks.
H100's 67 TFLOPS FP32 outperforms RTX 5070's 40.6 TFLOPS for simulations needing high precision and bandwidth.
Frequently Asked Questions
Which GPU has more VRAM?▾
The H100 provides 80 to 94 GB HBM3 VRAM, far exceeding the RTX 5070's 12 GB GDDR7. This allows H100 to load larger AI models without issues.
How do their FP16 performances compare?▾
H100 achieves 1979 TFLOPS in FP16, while RTX 5070 reaches 40.6 TFLOPS. H100 processes AI training nearly 49 times faster.
What is the memory bandwidth difference?▾
H100 offers 3350 GB/s, compared to RTX 5070's 448 GB/s. Higher bandwidth on H100 supports bigger batch sizes in deep learning.
Which is cheaper in the cloud?▾
RTX 5070 starts at $0.08 per hour averaging $0.17 across 4 offers, versus H100's $0.80 minimum and $3.14 average across 57 offers. RTX 5070 suits low-budget tasks.
What are their power consumptions?▾
H100 has a 700W TDP for datacenter use, while RTX 5070 uses 250W ideal for consumer setups. Lower TDP reduces operational costs for RTX 5070.
Which architecture is newer?▾
RTX 5070 uses Blackwell from 2025, succeeding Hopper in H100 from 2022. Blackwell focuses on efficiency despite lower peak specs.
Which is cheaper to rent, the H100 or the RTX 5070?▾
Cloud rental prices for both the H100 and RTX 5070 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 RTX 5070?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 5070 has 12 GB of GDDR7 memory.
Can I find H100 and RTX 5070 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 RTX 5070?▾
The H100 uses the Hopper architecture (2022) while the RTX 5070 uses Blackwell (2025). The H100 delivers 48.7x the FP16 throughput and 7.5x the memory bandwidth of the RTX 5070.