Specifications Compared
| Spec | GTX-1080 | V100 |
|---|---|---|
| TDP | 180W | 300W |
| VRAM | 8-11 GB | 16-32 GB |
| CUDA Cores | 2,560 | 5,120 |
| Memory Type | GDDR5X | HBM2 |
| Architecture | Pascal | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| FP16 Performance | 8.9 TFLOPS | 125 TFLOPS |
| FP32 Performance | 8.9 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 320 GB/s | 900 GB/s |
Performance Analysis
The V100's 125 TFLOPS FP16 performance vastly exceeds the GTX 1080's 8.9 TFLOPS, transforming training workflows through tensor core acceleration in mixed-precision setups. This enables up to 14-fold speedups in deep learning training, where FP16 dominates. FP32 throughput of 15.7 TFLOPS on V100 also outpaces GTX 1080's 8.9 TFLOPS, benefiting inference tasks requiring precise single-precision calculations.
Memory bandwidth disparity proves critical: V100's 900 GB/s versus GTX 1080's 320 GB/s allows larger batch sizes in training, minimizing data loading bottlenecks and improving GPU utilization. For example, models with high memory demands fit comfortably in V100's 16 GB HBM2, unlike the GTX 1080's 8 to 11 GB GDDR5X limit.
Power draw reflects these gains: V100's 300W TDP supports intensive compute, while GTX 1080's 180W suits lighter loads. In real-world AI pipelines, V100 handles complex simulations and large-scale inference far more efficiently due to NVLink interconnects enabling 2.4 TB/s inter-GPU communication.
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 |
Tesla V100 16GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | Texas | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 0 vCPU 0GB RAM | New York City | $0.19/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | Texas | $0.29/GPU/hr | Available | ||
TensorDock | NVIDIA Tesla V100 32GB 32GB VRAM | 32GB | 0 vCPU 0GB RAM | New York City | $0.29/GPU/hr | Available | ||
 Lambda Labs | 8×NVIDIA Tesla V100 16GB 16GB VRAM | 16GB | 88 vCPU 448GB RAM 6041GB Storage | Texas | $0.79/GPU/hr $6.32/hr total (8×) | Available |
When to Choose the GTX 1080
The GTX 1080 serves best in low-power, budget-constrained environments: its 180W TDP uses half the energy of V100's 300W, ideal for edge inference or small-scale prototyping. At $0.30 per hour average pricing across one offer, it fits hobbyist ML tasks where 8.9 TFLOPS FP32 and 8 to 11 GB VRAM suffice without needing V100's datacenter features.
Scenarios like basic computer vision inference or legacy gaming workloads favor GTX 1080, as PCIe form factor simplifies single-node deployments without NVLink complexity.
When to Choose the Tesla V100 16GB
The V100 excels in high-throughput AI training and inference: 125 TFLOPS FP16 accelerates large model optimization beyond GTX 1080's 8.9 TFLOPS capability. Its 900 GB/s bandwidth and 16 GB HBM2 VRAM manage massive batch sizes and datasets, crucial for production-scale deep learning.
Multi-GPU clusters benefit from NVLink and SXM2/PCIe form factors, scaling performance unavailable on GTX 1080. Despite averaging $0.82 per hour, deals from $0.10 per hour make it viable for demanding scientific computing.
Use Cases
V100's 125 TFLOPS FP16 enables rapid training of billion-parameter LLMs, outperforming GTX 1080's 8.9 TFLOPS by over 14 times in mixed precision.
16 GB HBM2 VRAM and 15.7 TFLOPS FP32 on V100 handle large language model deployments efficiently, exceeding GTX 1080's 8 to 11 GB and 8.9 TFLOPS limits.
900 GB/s bandwidth supports bigger batches during fine-tuning on V100, reducing epochs compared to GTX 1080's 320 GB/s constraint.
V100's high FP16 throughput and VRAM generate images faster; 125 TFLOPS crushes GTX 1080's 8.9 TFLOPS for diffusion model inference.
V100's 15.7 TFLOPS FP32 and NVLink scaling tackle complex simulations; GTX 1080 suffices only for simpler tasks at 8.9 TFLOPS.
Frequently Asked Questions
Which has more VRAM: GTX 1080 or V100 16GB?▾
The V100 16GB provides 16 GB HBM2 VRAM, doubling the GTX 1080's typical 8 GB GDDR5X. This allows V100 to load larger models without swapping. Bandwidth also favors V100 at 900 GB/s over 320 GB/s.
Is V100 faster for AI training than GTX 1080?▾
V100's 125 TFLOPS FP16 outperforms GTX 1080's 8.9 TFLOPS by a factor of 14, accelerating mixed-precision training. FP32 stands at 15.7 TFLOPS versus 8.9 TFLOPS. Tensor cores on V100 enable this edge.
What is the power consumption difference?▾
GTX 1080 draws 180W TDP, half of V100's 300W. Lower power suits GTX 1080 for efficient small jobs. V100 justifies higher draw with superior 125 TFLOPS FP16.
How do cloud prices compare?▾
GTX 1080 averages $0.30 per hour from one offer. V100 16GB starts at $0.10 per hour, averaging $0.82 across 27 offers. Deals make V100 competitive for performance.
Does V100 support multi-GPU better?▾
V100 includes NVLink for high-speed interconnects up to 300 GB/s per link, absent in PCIe-only GTX 1080. This scales clusters effectively. Form factors like SXM2 enhance datacenter use.
What architectures do they use?▾
GTX 1080 runs Pascal from 2016 with 8.9 TFLOPS FP32. V100 uses Volta from 2017, delivering 15.7 TFLOPS FP32 and 125 TFLOPS FP16. Volta introduces tensor cores for AI.
Which is cheaper to rent, the GTX 1080 or the V100?▾
Cloud rental prices for both the GTX 1080 and V100 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 V100?▾
The GTX 1080 has 8 to 11 GB of GDDR5X memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find GTX 1080 and V100 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 V100?▾
The GTX 1080 uses the Pascal architecture (2016) while the V100 uses Volta (2017). The V100 delivers 14.0x the FP16 throughput and 2.8x the memory bandwidth of the GTX 1080.