Specifications Compared
| Spec | H100 | RTX-3070 |
|---|---|---|
| TDP | 700W | 220W |
| VRAM | 80-94 GB | 8 GB |
| CUDA Cores | 16,896 | 5,888 |
| Memory Type | HBM3 | GDDR6 |
| Architecture | Hopper | Ampere |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| Tensor Cores | 528 | 184 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 20.3 TFLOPS |
| FP32 Performance | 67 TFLOPS | 20.3 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 3,350 GB/s | 448 GB/s |
Performance Analysis
The H100 SXM5's FP16 performance of 1979 TFLOPS enables rapid training of large neural networks, processing computations nearly 100 times faster than the RTX 3070's 20.3 TFLOPS. This delta accelerates deep learning pipelines, reducing training times from days to hours for models with billions of parameters. FP32 throughput on the H100 reaches 67 TFLOPS, tripling the RTX 3070's 20.3 TFLOPS for precision-sensitive tasks like simulations.
Memory bandwidth profoundly impacts real-world usage: the H100's 3350 GB/s supports massive batch sizes in training, minimizing data loading bottlenecks for large language models. The RTX 3070's 448 GB/s restricts it to smaller batches, often requiring model sharding or reduced resolutions. VRAM disparity seals the gap: 80 GB on the H100 accommodates full model loading for inference on 70B-parameter LLMs, while 8 GB on the RTX 3070 limits it to sub-7B models without quantization.
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 demanding AI workloads such as training large language models: its 1979 TFLOPS FP16 and 80 GB HBM3 VRAM handle datasets and parameters infeasible on consumer GPUs. Datacenter interconnects like NVLink and PCIe 5.0 enable multi-GPU scaling for distributed training at 3350 GB/s bandwidth. Cloud users prioritize it when time-to-results justifies $0.80 to $3.56 per hour costs.
When to Choose the RTX 3070
The RTX 3070 excels in budget-conscious scenarios like hobbyist prototyping or lightweight inference: 20.3 TFLOPS FP16 suffices for fine-tuning small models at $0.04 per hour. Its 220W TDP and PCIe form factor suit single-user desktops or low-power cloud instances. Developers choose it for Stable Diffusion generation or testing where 8 GB VRAM meets needs without overprovisioning.
Use Cases
The H100 SXM5's 1979 TFLOPS FP16 and 80 GB VRAM enable efficient training of billion-parameter models with large batch sizes. The RTX 3070's 8 GB VRAM and 20.3 TFLOPS cannot handle such scales.
H100 SXM5 supports high-throughput inference on large models via 3350 GB/s bandwidth and FP8 at 3958 TFLOPS. RTX 3070 is constrained by 448 GB/s and 8 GB VRAM for smaller models only.
H100's 67 TFLOPS FP32 and vast VRAM accelerate fine-tuning of mid-to-large models. RTX 3070 works for tiny models but bottlenecks on memory-intensive tasks.
RTX 3070's 20.3 TFLOPS FP16 generates images quickly at low $0.04 per hour cost with 8 GB VRAM sufficient for standard resolutions. H100 overkill for single-user creative tasks.
H100 SXM5's 67 TFLOPS FP32 and NVLink interconnect excel in parallel simulations. RTX 3070's lower specs limit complex computations.
Frequently Asked Questions
What is the VRAM difference between H100 SXM5 and RTX 3070?▾
The H100 SXM5 provides 80 to 94 GB HBM3 VRAM, enabling large model handling. The RTX 3070 offers 8 GB GDDR6, suitable for smaller workloads. This 10-fold gap affects batch sizes and model capacity.
How do cloud prices compare for these GPUs?▾
H100 SXM5 rentals start at $0.80 per hour, averaging $3.56 across 33 offers. RTX 3070 begins at $0.04 per hour, averaging $0.09 across 4 offers. Pricing aligns with performance disparities.
Which has higher FP16 performance?▾
H100 SXM5 delivers 1979 TFLOPS FP16, nearly 100 times the RTX 3070's 20.3 TFLOPS. This boosts AI training speed significantly. Inference also benefits from the H100's FP8 at 3958 TFLOPS.
Can RTX 3070 handle LLM inference?▾
RTX 3070 manages inference on models under 7B parameters with its 8 GB VRAM and 20.3 TFLOPS. Larger models require quantization or offloading. H100 SXM5 handles 70B models natively.
What is the memory bandwidth gap?▾
H100 SXM5 achieves 3350 GB/s, supporting huge batch sizes in training. RTX 3070 provides 448 GB/s, limiting data throughput. This difference impacts large-scale ML efficiency.
Which GPU has higher power consumption?▾
H100 SXM5 draws 700W TDP for datacenter use. RTX 3070 uses 220W, ideal for consumer setups. Power scales with compute capability.
Which is cheaper to rent, the H100 or the RTX 3070?▾
Cloud rental prices for both the H100 and RTX 3070 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 3070?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 3070 has 8 GB of GDDR6 memory.
Can I find H100 and RTX 3070 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 3070?▾
The H100 uses the Hopper architecture (2022) while the RTX 3070 uses Ampere (2020). The H100 delivers 97.5x the FP16 throughput and 7.5x the memory bandwidth of the RTX 3070.