Specifications Compared
| Spec | GH200 | GTX-1080 |
|---|---|---|
| TDP | 900W | 180W |
| VRAM | 96 GB | 8-11 GB |
| CUDA Cores | 16,896 | 2,560 |
| Memory Type | HBM3 | GDDR5X |
| Architecture | Hopper | Pascal |
| Form Factors | SXM | PCIe |
| Interconnect | NVLink-C2C, PCIe 5.0 | |
| Tensor Cores | 528 | |
| FP8 Performance | 3,958 TFLOPS | |
| FP16 Performance | 1,979 TFLOPS | 8.9 TFLOPS |
| FP32 Performance | 67 TFLOPS | 8.9 TFLOPS |
| FP64 Performance | 34 TFLOPS | |
| INT8 Performance | 3,958 TOPS | |
| Memory Bandwidth | 4,000 GB/s | 320 GB/s |
Performance Analysis
Compute performance separates these GPUs profoundly: GH200 achieves 1979 TFLOPS in FP16 and 67 TFLOPS in FP32, while GTX 1080 Ti matches 8.9 TFLOPS in both formats. This FP16 dominance on GH200 accelerates AI training and inference using half-precision, reducing memory use and speeding iterations by factors exceeding 200 times over GTX 1080 Ti equivalents. FP32 parity on GTX 1080 Ti suits legacy graphics but falters in modern tensor operations.
Memory bandwidth underscores scalability gaps: GH200's 4000 GB/s enables large batch sizes for training models with billions of parameters, minimizing data starvation. GTX 1080 Ti's 320 GB/s constrains such workloads to small batches, prolonging runtimes. GH200's FP8 at 3958 TFLOPS further boosts inference efficiency. Power draw reflects intent: 900W TDP for GH200 suits data centers, versus 180W for GTX 1080 Ti desktop use.
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 |
GTX 1080 Ti
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 4×NVIDIA GeForce GTX 1080 8GB VRAM | 8GB | 0 vCPU 64GB RAM 480GB Storage | Netherlands | $0.30/GPU/hr $1.20/hr total (4×) | Available | ||
LeaderGPU | 8×NVIDIA GeForce GTX 1080 Ti 11GB VRAM | 11GB | 0 vCPU 128GB RAM 480GB Storage | Netherlands | $0.60/GPU/hr $4.80/hr total (8×) | Available |
When to Choose the GH200 Grace Hopper
Select the GH200 for large-scale AI training or inference where 96 GB HBM3 VRAM handles models exceeding 70 billion parameters without multi-GPU splitting. Its 1979 TFLOPS FP16 and 4000 GB/s bandwidth support high-throughput scientific simulations or LLM fine-tuning in cloud clusters via NVLink-C2C. At $1.99 per hour starting price, it justifies cost for production workloads demanding speed.
When to Choose the GTX 1080 Ti
Choose the GTX 1080 Ti for budget-constrained prototyping of small neural networks or legacy gaming ports, leveraging 8.9 TFLOPS FP32 at $0.60 per hour. Its 180W TDP and PCIe form factor fit light cloud instances without power overhead. Suitable for quick Stable Diffusion tests under 512x512 resolution where 8 to 11 GB VRAM suffices.
Use Cases
GH200's 96 GB HBM3 VRAM and 1979 TFLOPS FP16 support training models with billions of parameters at large batch sizes via 4000 GB/s bandwidth. GTX 1080 Ti's 8 to 11 GB VRAM limits it to tiny models.
GH200's 3958 TFLOPS FP8 and high bandwidth enable serving multiple large LLMs concurrently. GTX 1080 Ti's 8.9 TFLOPS FP16 cannot match throughput demands.
GH200 handles full fine-tuning of 70B+ models with 67 TFLOPS FP32 and vast VRAM. GTX 1080 Ti restricts to LoRA on small models due to memory constraints.
GTX 1080 Ti generates 512x512 images adequately with 8.9 TFLOPS and 8 to 11 GB VRAM at low cost. GH200 excels for high-resolution or batch generation with superior speed.
GH200's 67 TFLOPS FP32 and 4000 GB/s bandwidth accelerate simulations like molecular dynamics. GTX 1080 Ti's 8.9 TFLOPS proves insufficient for complex datasets.
Frequently Asked Questions
What is the VRAM capacity of GH200 versus GTX 1080 Ti?▾
GH200 provides 96 GB HBM3 VRAM for massive models. GTX 1080 Ti offers 8 to 11 GB GDDR5X, suitable for smaller workloads.
Which GPU has higher FP16 performance?▾
GH200 delivers 1979 TFLOPS FP16, over 222 times the GTX 1080 Ti's 8.9 TFLOPS. This gap favors GH200 in AI acceleration.
How do cloud prices compare?▾
GH200 starts at $1.99 per hour, averaging $3.33 across five offers. GTX 1080 Ti is $0.60 per hour across one offer.
What are the memory bandwidth differences?▾
GH200 achieves 4000 GB/s, enabling large batches. GTX 1080 Ti reaches 320 GB/s, which bottlenecks scaled training.
Which has higher power consumption?▾
GH200 requires 900W TDP for data center use. GTX 1080 Ti uses 180W, ideal for lighter setups.
Can GTX 1080 Ti handle modern AI tasks?▾
GTX 1080 Ti manages small-scale inference or fine-tuning with 8.9 TFLOPS. It falls short for LLMs needing over 11 GB VRAM.
Which is cheaper to rent, the GH200 or the GTX 1080?▾
Cloud rental prices for both the GH200 and GTX 1080 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 GTX 1080?▾
The GH200 has 96 GB of HBM3 memory. The GTX 1080 has 8 to 11 GB of GDDR5X memory.
Can I find GH200 and GTX 1080 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 GTX 1080?▾
The GH200 uses the Hopper architecture (2023) while the GTX 1080 uses Pascal (2016). The GH200 delivers 222.4x the FP16 throughput and 12.5x the memory bandwidth of the GTX 1080.