Specifications Compared
| Spec | GH200 | RTX-4070 |
|---|---|---|
| TDP | 900W | 200W |
| VRAM | 96 GB | 12 GB |
| CUDA Cores | 16,896 | 5,888 |
| Memory Type | HBM3 | GDDR6X |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM | PCIe |
| Interconnect | NVLink-C2C, PCIe 5.0 | |
| Tensor Cores | 528 | 184 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 29.1 TFLOPS |
| FP32 Performance | 67 TFLOPS | 29.1 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | 466 TOPS |
| Memory Bandwidth | 4,000 GB/s | 504 GB/s |
Performance Analysis
Compute disparities define their capabilities: the GH200 delivers 1979 TFLOPS in FP16 versus 35 TFLOPS on the RTX 4070 SUPER, accelerating deep learning training where half-precision dominates. The GH200's FP32 at 67 TFLOPS also surpasses the SUPER's 35 TFLOPS, but FP8 at 3958 TFLOPS enables quantized inference at scales impossible for the Ada GPU. This FP16 to FP32 ratio on GH200 favors mixed-precision workflows in large models.
Memory bandwidth creates bottlenecks for the RTX 4070 SUPER: 4000 GB/s on GH200 supports batch sizes for billion-parameter LLMs, while 504 GB/s limits it to smaller datasets. VRAM of 96 GB versus 12 GB determines model size feasibility; GH200 handles full precision giants, RTX 4070 SUPER requires heavy optimization. Power draw of 900W versus 220W reflects data center versus desktop priorities, impacting cloud costs.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
GH200 Grace Hopper
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vultr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 960GB Storage | Atlanta | $1.99/GPU/hr | Available | ||
 Lambda Labs | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 64 vCPU 432GB RAM 4096GB Storage | Virginia | $2.29/GPU/hr | Available | ||
 Denvr | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 7600GB Storage | Virginia | $3.87/GPU/hr | |||
 CoreWeave | NVIDIA GH200 Grace Hopper 96GB VRAM | 96GB | 72 vCPU 480GB RAM 7680GB Storage | United States | $6.50/GPU/hr |
RTX 4070 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4070 Ti 12GB VRAM | 12GB | 6 vCPU 30GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the GH200 Grace Hopper
Select the GH200 for enterprise AI training and inference: 96 GB HBM3 VRAM accommodates massive LLMs, and 4000 GB/s bandwidth enables large batches without slowdowns. Its 1979 TFLOPS FP16 and NVLink-C2C interconnect scale across nodes, ideal for scientific simulations or HPC at $1.99 per hour starting price.
When to Choose the RTX 4070 SUPER
Choose the RTX 4070 SUPER for gaming, content creation, or small-scale ML: 12 GB GDDR6X suffices for Stable Diffusion or fine-tuning compact models, with 220W TDP minimizing power costs. Lacking cloud offers, it fits local workstations where 35 TFLOPS FP16 handles everyday inference efficiently.
Use Cases
GH200's 96 GB VRAM and 1979 TFLOPS FP16 support training billion-parameter models with large batches. RTX 4070 SUPER's 12 GB limits scale.
3958 TFLOPS FP8 and 4000 GB/s bandwidth on GH200 deliver high-throughput quantized serving. 12 GB VRAM on SUPER restricts model size.
Smaller models fit RTX 4070 SUPER's 12 GB VRAM for cost-effective tuning at 35 TFLOPS FP16. GH200 excels for parameter-heavy fine-tuning.
RTX 4070 SUPER's Ada architecture optimizes image generation with 504 GB/s bandwidth. GH200 overkill for consumer creative tasks.
GH200's 67 TFLOPS FP32 and NVLink-C2C handle simulations at scale. SUPER's 220W suits desktops but lacks enterprise interconnects.
Frequently Asked Questions
What is the VRAM difference between GH200 and RTX 4070 SUPER?▾
GH200 provides 96 GB HBM3 VRAM, while RTX 4070 SUPER has 12 GB GDDR6X. This allows GH200 to load massive models without swapping.
How do their FP16 performances compare?▾
GH200 achieves 1979 TFLOPS FP16, dwarfing RTX 4070 SUPER's 35 TFLOPS. The gap accelerates AI training by orders of magnitude.
What are the cloud prices for these GPUs?▾
GH200 starts at $1.99 per hour, averaging $3.59 per hour across four providers. No live cloud offers exist for RTX 4070 SUPER.
Which has higher memory bandwidth?▾
GH200 offers 4000 GB/s, versus 504 GB/s on RTX 4070 SUPER. Higher bandwidth reduces bottlenecks in large-batch processing.
What are their TDPs?▾
GH200 consumes 900W for data center use, RTX 4070 SUPER uses 220W for efficiency. Lower TDP lowers costs in small deployments.
Does RTX 4070 SUPER support FP8?▾
No, RTX 4070 SUPER lacks listed FP8 performance, unlike GH200's 3958 TFLOPS. GH200 suits advanced quantized inference.
Which is cheaper to rent, the GH200 or the RTX 4070?▾
Cloud rental prices for both the GH200 and RTX 4070 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 GH200 have compared to the RTX 4070?▾
The GH200 has 96 GB of HBM3 memory. The RTX 4070 has 12 GB of GDDR6X memory.
Can I find GH200 and RTX 4070 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 GH200 and the RTX 4070?▾
The GH200 uses the Hopper architecture (2023) while the RTX 4070 uses Ada Lovelace (2023). The GH200 delivers 68.0x the FP16 throughput and 7.9x the memory bandwidth of the RTX 4070.