Specifications Compared
| Spec | A100 | GTX-1080 |
|---|---|---|
| TDP | 400W | 180W |
| VRAM | 40-80 GB | 8-11 GB |
| CUDA Cores | 6,912 | 2,560 |
| Memory Type | HBM2e | GDDR5X |
| Architecture | Ampere | Pascal |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | |
| Tensor Cores | 432 | |
| FP16 Performance | 312 TFLOPS | 8.9 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 8.9 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 320 GB/s |
Performance Analysis
Compute differences define the A100's dominance over the GTX 1080 Ti. The A100 delivers 312 TFLOPS in FP16, 35 times the GTX 1080 Ti's 8.9 TFLOPS: this accelerates deep learning training where half-precision dominates. FP32 performance reaches 19.5 TFLOPS on A100 versus 8.9 TFLOPS on GTX 1080 Ti, a 2.2 times edge for general simulations. In real-world terms, training a large model on A100 completes far quicker due to these metrics. Memory specs further the gap: 2039 GB/s bandwidth on A100 versus 320 GB/s on GTX 1080 Ti enables batch sizes 6.4 times larger, minimizing iterations and boosting efficiency in inference pipelines. The A100's 40 GB VRAM loads models exceeding 11 GB, impossible on GTX 1080 Ti. Power draw reflects intent: 400W TDP fuels A100's throughput, while 180W on GTX 1080 Ti limits scale but aids efficiency in small tasks.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 SXM4 40GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 2826GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 794GB Storage | Slovenia | $0.73/GPU/hr $1.47/hr total (2×) | Available | ||
 LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 63GB RAM 557GB Storage | Czechia | $1.00/GPU/hr | Available | ||
 Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
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 A100 SXM4 40GB
Select the A100 SXM4 40GB for intensive machine learning tasks. Its 312 TFLOPS FP16 and 40 GB VRAM handle LLM training and fine-tuning of models too large for the GTX 1080 Ti's 8.9 TFLOPS and 11 GB limit. The 2039 GB/s bandwidth supports massive batches, cutting training time significantly. Production inference benefits from NVLink interconnects for multi-GPU scaling.
When to Choose the GTX 1080 Ti
The GTX 1080 Ti fits low-budget or lightweight workloads. Choose it for basic inference or hobbyist projects at $0.60 per hour, far below A100's $1.00 per hour start. Its 8.9 TFLOPS and 180W TDP suffice for small models under 11 GB VRAM, ideal for edge devices or testing where power and cost constrain options.
Use Cases
A100's 312 TFLOPS FP16 and 40 GB VRAM enable training massive LLMs, far beyond GTX 1080 Ti's 8.9 TFLOPS and 11 GB.
2039 GB/s bandwidth on A100 supports high-throughput inference with large batches; GTX 1080 Ti's 320 GB/s limits scale.
19.5 TFLOPS FP32 and 40 GB VRAM on A100 accelerate fine-tuning complex models unavailable on GTX 1080 Ti.
GTX 1080 Ti runs basic Stable Diffusion at 8.9 TFLOPS for $0.60 per hour; A100 boosts speed for advanced batches.
A100's 19.5 TFLOPS FP32 and NVLink suit parallel simulations; GTX 1080 Ti lacks interconnects for scale.
Frequently Asked Questions
Does A100 have more VRAM than GTX 1080 Ti?▾
A100 SXM4 40GB provides 40 GB HBM2e VRAM. GTX 1080 Ti offers 11 GB GDDR5X. A100 handles larger AI models as a result.
Is A100 better for deep learning training?▾
A100 achieves 312 TFLOPS FP16 versus GTX 1080 Ti's 8.9 TFLOPS. Training speeds increase by up to 35 times on A100.
How do cloud prices compare?▾
A100 starts at $1.00 per hour, averaging $2.63 per hour over five offers. GTX 1080 Ti is $0.60 per hour average from one offer.
Can GTX 1080 Ti handle AI inference?▾
GTX 1080 Ti supports inference with 8.9 TFLOPS FP16 and 11 GB VRAM for small models. Larger workloads need A100's 40 GB.
What are the TDP differences?▾
A100 has 400W TDP for peak performance. GTX 1080 Ti uses 180W, better for power-limited environments.
Which architecture is newer?▾
A100 uses Ampere from 2020 with PCIe 4.0 and NVLink. GTX 1080 Ti employs Pascal from 2016.
Which is cheaper to rent, the A100 or the GTX 1080?▾
Cloud rental prices for both the A100 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 A100 have compared to the GTX 1080?▾
The A100 has 40 to 80 GB of HBM2e memory. The GTX 1080 has 8 to 11 GB of GDDR5X memory.
Can I find A100 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 A100 and the GTX 1080?▾
The A100 uses the Ampere architecture (2020) while the GTX 1080 uses Pascal (2016). The A100 delivers 35.1x the FP16 throughput and 6.4x the memory bandwidth of the GTX 1080.