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 FP16 performance disparity is stark: the GTX 1080 delivers 8.9 TFLOPS, while the H100 SXM5 achieves 1979 TFLOPS, a 222 times improvement ideal for deep learning training and inference where half-precision computations dominate. For FP32, the H100 SXM5's 67 TFLOPS surpasses the GTX 1080's 8.9 TFLOPS by over 7.5 times, benefiting general compute tasks. This enables the H100 SXM5 to handle massive models that the GTX 1080 cannot due to limited 8 to 11 GB VRAM.
Memory bandwidth tells a similar story: 3350 GB/s on the H100 SXM5 versus 320 GB/s on the GTX 1080 allows for larger batch sizes in training, reducing overhead and speeding up iterations. In inference, high bandwidth supports low-latency serving of large language models. The H100 SXM5's FP8 capability at 3958 TFLOPS further accelerates quantized inference, unavailable on the GTX 1080. Higher TDP of 700W on the H100 SXM5 reflects its datacenter form factor, contrasting the GTX 1080's efficient 180W for edge use.
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 SXM5
| 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 |
When to Choose the GTX 1080
Choose the GTX 1080 for cost-sensitive gaming, light video editing, or basic machine learning prototypes where 8.9 TFLOPS FP32 suffices and 8 to 11 GB VRAM handles small datasets. Its $0.30 per hour pricing and 180W TDP make it ideal for low-power, intermittent cloud sessions without needing high interconnects.
When to Choose the H100 SXM5
Opt for the H100 SXM5 in professional AI training, large-scale inference, or scientific simulations requiring 80 to 94 GB HBM3 VRAM and 3350 GB/s bandwidth to process models beyond the GTX 1080's capacity. NVLink support enables multi-GPU scaling at $3.54 per hour average, justifying the cost for high-throughput datacenter deployments.
Use Cases
The H100 SXM5's 1979 TFLOPS FP16 and 80 to 94 GB HBM3 VRAM support large-scale training with big batch sizes, unlike the GTX 1080's limited 8.9 TFLOPS and 8 to 11 GB GDDR5X.
High FP16 at 1979 TFLOPS and 3350 GB/s bandwidth on the H100 SXM5 enable low-latency serving of massive models; the GTX 1080's 8.9 TFLOPS cannot compete.
Fine-tuning demands high VRAM and bandwidth: H100 SXM5 offers 80 to 94 GB and 3350 GB/s versus GTX 1080's 8 to 11 GB and 320 GB/s.
GTX 1080 handles basic Stable Diffusion at 8.9 TFLOPS FP16 for small images; H100 SXM5 excels for high-resolution or batched generation with 1979 TFLOPS.
H100 SXM5's 67 TFLOPS FP32 and NVLink interconnect scale complex simulations; GTX 1080's 8.9 TFLOPS limits it to modest tasks.
Frequently Asked Questions
What is the performance difference between GTX 1080 and H100 SXM5?▾
The H100 SXM5 delivers 1979 TFLOPS FP16 versus the GTX 1080's 8.9 TFLOPS, a 222-fold increase. FP32 is 67 TFLOPS on H100 SXM5 compared to 8.9 TFLOPS on GTX 1080.
How much VRAM do GTX 1080 and H100 SXM5 have?▾
GTX 1080 provides 8 to 11 GB GDDR5X. H100 SXM5 offers 80 to 94 GB HBM3, enabling much larger models.
What are the cloud rental prices for these GPUs?▾
GTX 1080 averages $0.30 per hour across 1 offer. H100 SXM5 starts at $0.80 per hour, averaging $3.54 per hour across 32 offers.
Is GTX 1080 suitable for AI training?▾
GTX 1080's 8.9 TFLOPS FP16 and 8 to 11 GB VRAM limit it to small models. H100 SXM5 with 1979 TFLOPS and 80 to 94 GB is far superior for training.
What is the memory bandwidth comparison?▾
GTX 1080 has 320 GB/s. H100 SXM5 achieves 3350 GB/s, over 10 times higher for better batch processing.
Which has higher power consumption?▾
H100 SXM5 TDP is 700W for datacenter use. GTX 1080 is 180W, more efficient for consumer tasks.
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.