Specifications Compared
| Spec | GTX-1080 | H100 |
|---|---|---|
| TDP | 180W | 700W |
| VRAM | 8-11 GB | 80-94 GB |
| CUDA Cores | 2,560 | 16,896 |
| Memory Type | GDDR5X | HBM3 |
| Architecture | Pascal | Hopper |
| Form Factors | PCIe | SXM5, PCIe, NVL |
| Interconnect | NVLink, PCIe 5.0, InfiniBand | |
| FP16 Performance | 8.9 TFLOPS | 1,979 TFLOPS |
| FP32 Performance | 8.9 TFLOPS | 67 TFLOPS |
| Memory Bandwidth | 320 GB/s | 3,350 GB/s |
Performance Analysis
The H100 vastly outperforms the GTX 1080 in compute capabilities critical for AI. Its FP16 performance reaches 1979 TFLOPS compared to the GTX 1080's 8.9 TFLOPS, enabling over 222 times faster half-precision training for deep learning models. FP32 on the H100 is 67 TFLOPS against 8.9 TFLOPS, a 7.5 times gain ideal for general scientific simulations. The equal FP16 and FP32 on GTX 1080 lacks tensor core optimizations present in Hopper, limiting mixed-precision efficiency. Memory bandwidth of 3350 GB/s on H100 versus 320 GB/s on GTX 1080 supports batch sizes up to 10 times larger, reducing training iterations for large language models. This disparity accelerates convergence in inference tasks, where H100's FP8 at 3958 TFLOPS handles quantized models at scales impossible on Pascal. Higher TDP of 700W on H100 demands robust cooling, unlike the 180W GTX 1080 suited for lighter setups.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
GTX 1080
| 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 |
H100
| 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 | ||
 Voltage Park | 8×NVIDIA H100 SXM5 80GB VRAM | 80GB | 208 vCPU 928GB RAM 19200GB Storage | Dallas, Texas | $1.99/GPU/hr $15.92/hr total (8×) |
When to Choose the GTX 1080
The GTX 1080 suits budget-constrained hobbyists or small-scale prototyping. With pricing from $0.30 per hour and 8 to 11 GB VRAM, it handles lightweight inference or fine-tuning of models under 7 billion parameters. Its 180W TDP fits consumer PCIe setups without specialized infrastructure, ideal for local experimentation before scaling.
When to Choose the H100
The H100 excels in production AI environments requiring high throughput. Its 80 to 94 GB HBM3 VRAM and 3350 GB/s bandwidth enable training of models exceeding 70 billion parameters, while 1979 TFLOPS FP16 supports rapid iterations. NVLink and PCIe 5.0 interconnects facilitate multi-GPU clusters unavailable on GTX 1080.
Use Cases
H100's 1979 TFLOPS FP16 and 3350 GB/s bandwidth enable training large models with massive batches. GTX 1080's 8.9 TFLOPS limits it to tiny models.
H100 supports 80 to 94 GB VRAM for serving models over 70B parameters at 3958 TFLOPS FP8. GTX 1080 restricts to small models with 8 to 11 GB.
H100's 67 TFLOPS FP32 and high bandwidth speed fine-tuning on large datasets. GTX 1080 suffices only for models under 1B parameters.
GTX 1080 generates images at 8.9 TFLOPS for basic use. H100 accelerates high-resolution batches with 1979 TFLOPS FP16.
H100's 67 TFLOPS FP32 outperforms GTX 1080's 8.9 TFLOPS for simulations. Its interconnects enable distributed computing.
Frequently Asked Questions
What is the performance difference in FP16 between GTX 1080 and H100?▾
H100 delivers 1979 TFLOPS FP16 versus GTX 1080's 8.9 TFLOPS, a 222-fold increase. This gap accelerates AI training significantly. Memory bandwidth also differs: 3350 GB/s on H100 against 320 GB/s.
How much VRAM do GTX 1080 and H100 have?▾
GTX 1080 provides 8 to 11 GB GDDR5X VRAM. H100 offers 80 to 94 GB HBM3. The H100 handles much larger models as a result.
What are the cloud rental prices for these GPUs?▾
GTX 1080 starts at $0.30 per hour, averaging $0.45 per hour across 2 offers. H100 begins at $0.80 per hour, averaging $3.17 per hour across 56 offers. Pricing reflects performance disparity.
Is GTX 1080 suitable for modern AI training?▾
GTX 1080's 8.9 TFLOPS FP16 limits it to small models under 1B parameters. H100's 1979 TFLOPS supports large-scale training. Legacy Pascal lacks tensor cores for efficiency.
What is the TDP and form factors comparison?▾
GTX 1080 has 180W TDP in PCIe form. H100 requires 700W in SXM5, PCIe, or NVL formats. H100 needs datacenter power infrastructure.
Can GTX 1080 compete with H100 in inference?▾
No, H100's 3958 TFLOPS FP8 and 80 to 94 GB VRAM enable high-throughput serving. GTX 1080's 8 to 11 GB restricts batch sizes severely.
Which is cheaper to rent, the GTX 1080 or the H100?▾
Cloud rental prices for both the GTX 1080 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 1080 have compared to the H100?▾
The GTX 1080 has 8 to 11 GB of GDDR5X memory. The H100 has 80 to 94 GB of HBM3 memory.
Can I find GTX 1080 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 1080 and the H100?▾
The GTX 1080 uses the Pascal architecture (2016) while the H100 uses Hopper (2022). The H100 delivers 222.4x the FP16 throughput and 10.5x the memory bandwidth of the GTX 1080.