Specifications Compared
| Spec | GH200 | RTX-4080 |
|---|---|---|
| TDP | 900W | 320W |
| VRAM | 96 GB | 16 GB |
| CUDA Cores | 16,896 | 9,728 |
| Memory Type | HBM3 | GDDR6X |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM | PCIe |
| Interconnect | NVLink-C2C, PCIe 5.0 | |
| Tensor Cores | 528 | 304 |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 48.7 TFLOPS |
| FP32 Performance | 67 TFLOPS | 48.7 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | 780 TOPS |
| Memory Bandwidth | 4,000 GB/s | 717 GB/s |
Performance Analysis
The GH200's FP16 performance of 1979 TFLOPS vastly outpaces the RTX 4080 SUPER's 48.7 TFLOPS, enabling faster model training where half-precision computations dominate. Its FP32 rate of 67 TFLOPS slightly exceeds the RTX 4080 SUPER's 48.7 TFLOPS, supporting precise scientific simulations. This FP16 to FP32 delta favors the GH200 for deep learning training, which relies heavily on FP16 tensor cores, while inference benefits from FP8 at 3958 TFLOPS on GH200. Memory bandwidth defines practical limits: the GH200's 4000 GB/s sustains large batch sizes in transformer models, preventing bottlenecks in LLM processing, whereas the RTX 4080 SUPER's 717 GB/s restricts it to smaller batches. VRAM disparity is stark: 96 GB on GH200 accommodates billion-parameter models without swapping, compared to 16 GB on RTX 4080 SUPER requiring model parallelism or quantization.
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 4080 SUPER
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4080 SUPER 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr | |||
RunPod | NVIDIA GeForce RTX 4080 16GB VRAM | 16GB | 6 vCPU 35GB RAM | 🌍global | $0.50/GPU/hr |
When to Choose the GH200 Grace Hopper
Opt for the GH200 in large-scale AI training and inference where 96 GB HBM3 VRAM handles models exceeding 70 billion parameters. Its 4000 GB/s bandwidth and 1979 TFLOPS FP16 throughput accelerate workflows like LLM fine-tuning by processing massive datasets without memory constraints. Enterprise users prioritize its NVLink-C2C interconnect for multi-GPU scaling in SXM form factor deployments.
When to Choose the RTX 4080 SUPER
Select the RTX 4080 SUPER for cost-sensitive tasks such as prototyping or gaming-integrated AI at $0.17 per hour average. Its 320W TDP and PCIe form factor suit edge computing or single-user workstations with 16 GB VRAM sufficient for Stable Diffusion or small-batch inference. Developers value its 48.7 TFLOPS FP16 for quick iterations without enterprise overhead.
Use Cases
The GH200's 1979 TFLOPS FP16 and 96 GB HBM3 VRAM support training of large language models with billion-scale parameters and high batch sizes. The RTX 4080 SUPER's 16 GB limits it to smaller models.
GH200's 3958 TFLOPS FP8 and 4000 GB/s bandwidth deliver low-latency inference for production-scale deployments. RTX 4080 SUPER suffices only for lightweight queries.
With 96 GB VRAM, GH200 accommodates full model fine-tuning without distillation. Its superior FP16 performance speeds up iterations over RTX 4080 SUPER's constraints.
RTX 4080 SUPER's 48.7 TFLOPS FP16 and $0.17 per hour pricing enable efficient image generation at consumer scale. GH200's power draw is excessive for this task.
GH200's 67 TFLOPS FP32 and NVLink interconnect excel in simulations requiring high precision and multi-GPU coordination. RTX 4080 SUPER handles basic tasks affordably.
Frequently Asked Questions
Which GPU has more VRAM, GH200 or RTX 4080 SUPER?▾
The GH200 provides 96 GB HBM3 VRAM, six times the RTX 4080 SUPER's 16 GB GDDR6X. This enables GH200 to load larger models without offloading. RTX 4080 SUPER suits memory-light applications.
How do GH200 and RTX 4080 SUPER compare in cloud pricing?▾
GH200 starts at $1.99 per hour averaging $3.59 across four offers. RTX 4080 SUPER is from $0.17 per hour at $0.32 average over three offers. Pricing reflects their data center versus consumer focus.
What is the FP16 performance difference between GH200 and RTX 4080 SUPER?▾
GH200 achieves 1979 TFLOPS FP16, over 40 times the RTX 4080 SUPER's 48.7 TFLOPS. This gap accelerates AI training significantly on GH200. Inference also benefits from GH200's FP8 at 3958 TFLOPS.
Which has higher memory bandwidth?▾
GH200 offers 4000 GB/s, over five times the RTX 4080 SUPER's 717 GB/s. Higher bandwidth on GH200 supports larger batch sizes in deep learning. RTX 4080 SUPER performs adequately for smaller workloads.
What are the TDP ratings for these GPUs?▾
GH200 consumes 900W TDP in SXM form factor for enterprise use. RTX 4080 SUPER uses 320W in PCIe, ideal for lower-power setups. Power needs align with workload scale.
Can RTX 4080 SUPER replace GH200 for AI training?▾
No, RTX 4080 SUPER's 16 GB VRAM and 48.7 TFLOPS FP16 cannot handle GH200-scale training of large models. It works for prototyping at lower cost. GH200 is essential for production training.
Which is cheaper to rent, the GH200 or the RTX 4080?▾
Cloud rental prices for both the GH200 and RTX 4080 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 4080?▾
The GH200 has 96 GB of HBM3 memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find GH200 and RTX 4080 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 4080?▾
The GH200 uses the Hopper architecture (2023) while the RTX 4080 uses Ada Lovelace (2022). The GH200 delivers 40.6x the FP16 throughput and 5.6x the memory bandwidth of the RTX 4080.