Specifications Compared
| Spec | GTX-1070 | H100 |
|---|---|---|
| TDP | 150W | 700W |
| VRAM | 8 GB | 80-94 GB |
| CUDA Cores | 1,920 | 16,896 |
| Memory Type | GDDR5 | HBM3 |
| Architecture | Pascal | Hopper |
| Form Factors | PCIe | SXM5, PCIe, NVL |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| FP16 Performance | 6.5 TFLOPS | 1,979 TFLOPS |
| FP32 Performance | 6.5 TFLOPS | 67 TFLOPS |
| Memory Bandwidth | 256 GB/s | 3,350 GB/s |
Performance Analysis
Raw compute disparities define usability: the GTX 1070's matched 6.5 TFLOPS FP16 and FP32 suits general graphics but falters in AI training, where FP16 acceleration is critical. The H100 SXM5's 1979 TFLOPS FP16 enables rapid model training on massive datasets, while its 67 TFLOPS FP32 outperforms for precision tasks. FP8 at 3958 TFLOPS on H100 accelerates inference for quantized large language models.
Memory specs reshape workloads profoundly: 8 GB GDDR5 on GTX 1070 with 256 GB/s bandwidth restricts batch sizes to small models, causing frequent data swaps. H100 SXM5's 80-94 GB HBM3 and 3350 GB/s bandwidth support enormous batches, reducing latency in training and inference for models exceeding 70 billion parameters.
Power and form factor influence deployment: GTX 1070's 150 W TDP fits desktops efficiently, whereas H100 SXM5's 700 W demands robust cooling and infrastructure, justified by cloud pricing from $0.80 per hour.
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 | ||
Hyperstack | 8×NVIDIA H100 PCIe 80GB VRAM | 80GB | 252 vCPU 1440GB RAM 6600GB Storage | Canada | $1.95/GPU/hr $15.60/hr total (8×) | Available |
When to Choose the GTX 1070
The GTX 1070 excels in budget-conscious gaming or legacy desktop applications: its 6.5 TFLOPS FP32 handles 1080p gaming at 60 FPS and light content creation. With 150 W TDP and PCIe compatibility, it integrates seamlessly into consumer PCs without high power demands. No live cloud offers make it ideal for on-premise setups where acquisition costs remain under $100 used.
When to Choose the H100 SXM5
The H100 SXM5 dominates AI and high-performance computing: 1979 TFLOPS FP16 accelerates deep learning training, while 80-94 GB VRAM manages large models infeasible on 8 GB alternatives. Cloud availability from $0.80 per hour across 32 offers suits scalable projects, with NVLink enabling multi-GPU clusters for distributed inference at 3958 TFLOPS FP8.
Use Cases
H100 SXM5's 1979 TFLOPS FP16 and 80-94 GB HBM3 VRAM handle massive parameter counts and large batches, unlike GTX 1070's 6.5 TFLOPS and 8 GB limit.
3958 TFLOPS FP8 on H100 SXM5 optimizes quantized inference for real-time serving, far exceeding GTX 1070's capabilities constrained by 256 GB/s bandwidth.
3350 GB/s bandwidth and 80-94 GB VRAM on H100 support efficient fine-tuning of large models, while GTX 1070's 8 GB restricts to tiny datasets.
H100 SXM5 generates high-resolution images rapidly via 1979 TFLOPS FP16, outperforming GTX 1070 where 8 GB VRAM limits image sizes and iteration speed.
67 TFLOPS FP32 and NVLink interconnects on H100 enable complex simulations across clusters, surpassing GTX 1070's isolated 6.5 TFLOPS setup.
Frequently Asked Questions
What is the VRAM difference between GTX 1070 and H100 SXM5?▾
GTX 1070 offers 8 GB GDDR5 VRAM. H100 SXM5 provides 80-94 GB HBM3. This gap allows H100 to process models up to 10 times larger without offloading.
How do FP16 performance levels compare?▾
GTX 1070 delivers 6.5 TFLOPS FP16. H100 SXM5 reaches 1979 TFLOPS, over 300 times higher. This boosts AI training speed dramatically on H100.
What are the power requirements?▾
GTX 1070 has 150 W TDP for efficient desktop use. H100 SXM5 requires 700 W, suited for datacenter cooling. Cloud deployments mitigate local power concerns.
Is H100 SXM5 available on cloud platforms?▾
H100 SXM5 starts at $0.80 per hour, averaging $3.54 per hour across 32 live offers. GTX 1070 has no current cloud availability. This favors H100 for on-demand scaling.
Can GTX 1070 handle modern AI workloads?▾
GTX 1070's 256 GB/s bandwidth and 8 GB VRAM limit it to small models. H100 SXM5's 3350 GB/s and 80-94 GB excel in LLM training and inference.
What interconnects do they support?▾
GTX 1070 uses PCIe only. H100 SXM5 includes NVLink, PCIe 5.0, and InfiniBand. These enable H100 multi-GPU scaling for distributed computing.
Which is cheaper to rent, the GTX 1070 or the H100?▾
Cloud rental prices for both the GTX 1070 and H100 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 GTX 1070 have compared to the H100?▾
The GTX 1070 has 8 GB of GDDR5 memory. The H100 has 80 to 94 GB of HBM3 memory.
Can I find GTX 1070 and H100 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 GTX 1070 and the H100?▾
The GTX 1070 uses the Pascal architecture (2016) while the H100 uses Hopper (2022). The H100 delivers 304.5x the FP16 throughput and 13.1x the memory bandwidth of the GTX 1070.