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
Volta's tensor cores in the V100 32GB deliver 125 TFLOPS FP16 versus the GTX 1080 Ti's 11.3 TFLOPS, accelerating mixed-precision training by up to 11x in deep learning frameworks like PyTorch. FP32 stands at 15.7 TFLOPS for V100 against 11.3 TFLOPS for 1080 Ti, supporting 39% faster single-precision inference or simulations. Memory bandwidth disparity is stark: 900 GB/s HBM2 on V100 enables larger batch sizes in training, such as 4x more samples per iteration than 1080 Ti's 484 GB/s GDDR5X limit, reducing epochs needed for convergence. In real-world terms, V100 handles LLM fine-tuning with 32 GB VRAM for models up to 7B parameters at batch size 16, while 1080 Ti caps at smaller 1-3B models with batch size 4 due to 11 GB constraint. Higher 300 W TDP on V100 sustains peaks longer than 1080 Ti's 250 W, vital for prolonged HPC runs.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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 |
Tesla V100 32GB
| 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 Ti
The GTX 1080 Ti suits cost-sensitive, low-VRAM workloads like lightweight inference on models under 3 GB or basic Stable Diffusion at 512x512 resolution. Its $0.60 per hour pricing undercuts V100's $1.01 average, ideal for prototyping on PCIe-only clouds without NVLink needs. Pascal's 11.3 TFLOPS FP32 handles scientific visualization or gaming ML ports efficiently at 250 W TDP.
When to Choose the Tesla V100 32GB
The V100 32GB excels in demanding AI training and large-model inference, leveraging 125 TFLOPS FP16 for 10x faster LLM optimization than GTX 1080 Ti. 32 GB HBM2 and 900 GB/s bandwidth support batch sizes up to 32 for 13B models, unavailable on 11 GB GDDR5X. NVLink interconnect scales multi-GPU setups, perfect for HPC at $0.29 per hour low-end pricing.
Use Cases
V100's 125 TFLOPS FP16 and 32 GB HBM2 handle large batch sizes for billion-parameter models. GTX 1080 Ti's 11 GB VRAM limits it to small-scale training.
32 GB VRAM on V100 supports 13B+ models at high throughput with 900 GB/s bandwidth. 1080 Ti caps at smaller models due to 11 GB and 484 GB/s.
Volta tensor cores deliver 11x FP16 speedup for efficient fine-tuning on 7B models. Pascal lacks this, slowing GTX 1080 Ti significantly.
GTX 1080 Ti runs 512x512 generations adequately at 11.3 TFLOPS FP32 for $0.60 per hour. V100 accelerates higher resolutions with 32 GB VRAM but at higher average cost.
15.7 TFLOPS FP32 and NVLink on V100 scale simulations better than 1080 Ti's 11.3 TFLOPS PCIe limit. Bandwidth supports large datasets.
Frequently Asked Questions
Which GPU has more VRAM?▾
The V100 32GB offers 32 GB HBM2 versus GTX 1080 Ti's 11 GB GDDR5X. This enables V100 to load larger models like 13B LLMs fully into memory. GTX 1080 Ti suits sub-7 GB workloads.
What is the FP16 performance difference?▾
V100 achieves 125 TFLOPS FP16 with tensor cores, 11x higher than GTX 1080 Ti's 11.3 TFLOPS. This accelerates training in TensorFlow by orders of magnitude. Inference gains are similar for half-precision.
How do cloud prices compare?▾
GTX 1080 Ti averages $0.60 per hour across offers. V100 32GB starts at $0.29 per hour but averages $1.01 across 46 offers. Budget users favor 1080 Ti for light tasks.
Which has higher memory bandwidth?▾
V100 provides 900 GB/s HBM2, nearly 2x GTX 1080 Ti's 484 GB/s GDDR5X. Larger batches in training reduce time-to-convergence on V100. 1080 Ti suffices for smaller datasets.
Is V100 better for multi-GPU setups?▾
V100 supports NVLink for 300 GB/s inter-GPU bandwidth, scaling 8x cards efficiently. GTX 1080 Ti relies on PCIe at lower speeds. Use V100 for distributed training.
What are the TDP ratings?▾
GTX 1080 Ti draws 250 W, lower than V100's 300 W. This makes 1080 Ti easier for power-constrained clouds. V100 sustains higher compute peaks longer.
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.