Specifications Compared
| Spec | H100 | RTX-4080 |
|---|---|---|
| TDP | 700W | 320W |
| VRAM | 80-94 GB | 16 GB |
| CUDA Cores | 16,896 | 9,728 |
| Memory Type | HBM3 | GDDR6X |
| Architecture | Hopper | Ada Lovelace |
| Form Factors | SXM5, PCIe, NVL | PCIe |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| 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 | 3,350 GB/s | 717 GB/s |
Performance Analysis
The H100 PCIe vastly outperforms the RTX 4080 SUPER in half-precision computing critical for modern AI: it achieves 1979 TFLOPS in FP16 compared to 48.7 TFLOPS, enabling up to 40 times faster model training and inference on large neural networks. This FP16 to FP32 balance on the H100, with 67 TFLOPS FP32, supports mixed-precision workflows better than the RTX 4080 SUPER's equal 48.7 TFLOPS in both, which suits graphics but limits scalability in professional training pipelines.
Memory specifications define real-world usability: the H100's 80 to 94 GB HBM3 VRAM and 3350 GB/s bandwidth accommodate massive batch sizes and models exceeding 16 GB, preventing out-of-memory errors common on the RTX 4080 SUPER. Higher bandwidth reduces data transfer bottlenecks during training epochs, allowing the H100 to process larger datasets efficiently. The RTX 4080 SUPER's 717 GB/s proves adequate for smaller batches but throttles performance on memory-intensive tasks like large language model fine-tuning.
Power consumption influences deployment: the H100's 700 W TDP demands robust cooling and infrastructure, yet yields superior throughput per watt in FP8 at 3958 TFLOPS. The RTX 4080 SUPER's 320 W enables denser cloud instances at lower cost, ideal for inference on modest models.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
H100 PCIe
| 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 |
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 H100 PCIe
Select the H100 PCIe for large-scale AI workloads requiring extensive memory and compute. Its 80 to 94 GB HBM3 VRAM handles models like 70B-parameter LLMs without quantization, while 1979 TFLOPS FP16 accelerates training epochs significantly faster than the RTX 4080 SUPER's 48.7 TFLOPS. Multi-GPU setups benefit from NVLink and PCIe 5.0 interconnects at $1.25 per hour starting price.
High-throughput inference scenarios favor the H100: 3958 TFLOPS FP8 supports serving thousands of requests per second on massive models.
When to Choose the RTX 4080 SUPER
The RTX 4080 SUPER excels in cost-sensitive prototyping and small-scale machine learning at $0.17 per hour. Its 16 GB GDDR6X VRAM suffices for fine-tuning models under 7B parameters or running Stable Diffusion, where 48.7 TFLOPS FP16 delivers responsive performance without the H100's overhead.
Budget-conscious users prioritize it for inference on lightweight models or scientific simulations fitting within 717 GB/s bandwidth and 320 W TDP.
Use Cases
The H100 PCIe supports massive batch sizes with 80 to 94 GB HBM3 VRAM and delivers 1979 TFLOPS FP16 for rapid training of large models. The RTX 4080 SUPER's 16 GB limits it to smaller scales.
3958 TFLOPS FP8 on the H100 PCIe enables high-throughput serving of billion-parameter models. The RTX 4080 SUPER handles only modest loads with 48.7 TFLOPS FP16.
16 GB GDDR6X on the RTX 4080 SUPER fits most fine-tuning tasks at $0.17 per hour. The H100 PCIe overpowers smaller datasets unnecessarily.
The RTX 4080 SUPER generates images efficiently with 48.7 TFLOPS FP16 and 717 GB/s bandwidth on 16 GB VRAM. Cost savings make it ideal over the H100.
67 TFLOPS FP32 and 3350 GB/s bandwidth on the H100 PCIe accelerate simulations with large datasets. The RTX 4080 SUPER's matching 48.7 TFLOPS FP32 falls short on memory-intensive jobs.
Frequently Asked Questions
What is the VRAM difference between H100 PCIe and RTX 4080 SUPER?▾
The H100 PCIe provides 80 to 94 GB HBM3 VRAM, far exceeding the RTX 4080 SUPER's 16 GB GDDR6X. This allows the H100 to manage larger AI models without memory constraints. The RTX 4080 SUPER suits smaller workloads.
How do cloud prices compare for these GPUs?▾
H100 PCIe rentals start from $1.25 per hour with an average of $2.62 per hour across 23 offers. RTX 4080 SUPER begins at $0.17 per hour averaging $0.32 per hour over 3 offers. Pricing reflects performance disparities.
Which GPU has higher FP16 performance?▾
The H100 PCIe achieves 1979 TFLOPS FP16, over 40 times the RTX 4080 SUPER's 48.7 TFLOPS. This gap accelerates AI training significantly on the H100. FP32 stands at 67 TFLOPS versus 48.7 TFLOPS.
What are the memory bandwidth specs?▾
H100 PCIe offers 3350 GB/s with HBM3, compared to 717 GB/s GDDR6X on RTX 4080 SUPER. Higher bandwidth on H100 supports larger batches in training. It reduces data bottlenecks in compute-heavy tasks.
How do TDPs differ?▾
The H100 PCIe consumes 700 W TDP, while RTX 4080 SUPER uses 320 W. Lower TDP enables cheaper, denser deployments for the consumer GPU. Datacenter infrastructure handles the H100's demands.
What architectures power these GPUs?▾
H100 PCIe uses Hopper architecture from 2022 optimized for AI. RTX 4080 SUPER employs Ada Lovelace from 2022 geared toward gaming and graphics. Both support modern tensor cores.
Which is cheaper to rent, the H100 or the RTX 4080?▾
Cloud rental prices for both the H100 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 H100 have compared to the RTX 4080?▾
The H100 has 80 to 94 GB of HBM3 memory. The RTX 4080 has 16 GB of GDDR6X memory.
Can I find H100 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 H100 and the RTX 4080?▾
The H100 uses the Hopper architecture (2022) while the RTX 4080 uses Ada Lovelace (2022). The H100 delivers 40.6x the FP16 throughput and 4.7x the memory bandwidth of the RTX 4080.