Specifications Compared
| Spec | RTX-2070 | V100 |
|---|---|---|
| TDP | 175W | 300W |
| VRAM | 8 GB | 16-32 GB |
| CUDA Cores | 2,304 | 5,120 |
| Memory Type | GDDR6 | HBM2 |
| Architecture | Turing | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink | NVLink, PCIe 3.0 |
| Tensor Cores | 288 | 640 |
| FP16 Performance | 7.5 TFLOPS | 125 TFLOPS |
| FP32 Performance | 7.5 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 448 GB/s | 900 GB/s |
Performance Analysis
The V100 16GB dominates in raw compute capacity: its 125 TFLOPS FP16 performance, driven by tensor cores, excels in mixed-precision training tasks common in deep learning, enabling faster convergence than the RTX 2070 SUPER's 9 TFLOPS FP16. The FP32 performance gap, 15.7 TFLOPS versus 9 TFLOPS, favors V100 for single-precision workloads like certain simulations, though both GPUs maintain balanced FP16-to-FP32 ratios suited to their architectures.
Memory specifications create practical implications for workloads: V100's 900 GB/s bandwidth and 16 GB HBM2 support larger batch sizes in training, reducing overhead in memory-bound scenarios such as transformer models, where the SUPER's 496 GB/s and 8 GB VRAM limit scalability. Higher TDP of 300 W on V100 correlates with sustained performance under load, while SUPER's 215 W suits power-constrained environments. Overall, V100 handles high-throughput inference and training better due to these metrics.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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 RTX 2070 SUPER
The RTX 2070 SUPER suits desktop setups or light machine learning inference where power efficiency matters: its 215 W TDP consumes less energy than V100's 300 W. Developers with local PCIe systems benefit from 8 GB GDDR6 at 496 GB/s for tasks like gaming-assisted compute or small-scale Stable Diffusion runs, especially since no cloud offers exist, avoiding rental costs.
When to Choose the Tesla V100 16GB
Opt for the V100 16GB in datacenter or cloud environments demanding high memory and bandwidth: 16 GB HBM2 at 900 GB/s enables large-batch training unavailable on the SUPER's 8 GB. Cloud availability from $0.10 per hour makes it ideal for scalable FP16-heavy workloads leveraging 125 TFLOPS.
Use Cases
V100 16GB's 125 TFLOPS FP16 and 16 GB HBM2 handle large models and batches better than SUPER's 9 TFLOPS FP16 and 8 GB GDDR6.
High 900 GB/s bandwidth and 125 TFLOPS FP16 on V100 support efficient high-throughput inference; SUPER's 496 GB/s limits scale.
V100's superior 15.7 TFLOPS FP32 and 16 GB VRAM accelerate fine-tuning on datasets too large for SUPER's 9 TFLOPS FP32 and 8 GB.
RTX 2070 SUPER's 9 TFLOPS FP32 and lower 215 W TDP suffice for consumer generative tasks; adequate 496 GB/s bandwidth fits typical image generation.
V100's 15.7 TFLOPS FP32 and NVLink interconnect optimize parallel simulations; outperforms SUPER's 9 TFLOPS FP32.
Frequently Asked Questions
Which GPU has more VRAM: RTX 2070 SUPER or V100 16GB?▾
The V100 16GB offers 16 GB HBM2, doubling the RTX 2070 SUPER's 8 GB GDDR6. This enables larger models on V100. Bandwidth also favors V100 at 900 GB/s over 496 GB/s.
How do FP16 performances compare between RTX 2070 SUPER and V100 16GB?▾
V100 16GB achieves 125 TFLOPS FP16 via tensor cores, vastly exceeding RTX 2070 SUPER's 9 TFLOPS. This gap accelerates mixed-precision training on V100. FP32 follows suit: 15.7 TFLOPS versus 9 TFLOPS.
What is the power consumption of RTX 2070 SUPER versus V100 16GB?▾
RTX 2070 SUPER draws 215 W TDP, lower than V100 16GB's 300 W. Lower power suits desktop use for SUPER. V100 sustains higher performance under datacenter loads.
Is cloud pricing available for these GPUs?▾
V100 16GB has 27 live cloud offers from $0.10 per hour, averaging $0.82 per hour. RTX 2070 SUPER currently has no live offers. V100 provides flexible rental options.
Which is better for machine learning training?▾
V100 16GB excels with 125 TFLOPS FP16, 16 GB VRAM, and 900 GB/s bandwidth for training. RTX 2070 SUPER's 9 TFLOPS FP16 and 8 GB VRAM limit it to smaller tasks.
What architectures do they use?▾
RTX 2070 SUPER employs Turing from 2019; V100 16GB uses Volta from 2017. V100's datacenter optimizations yield higher compute like 125 TFLOPS FP16.
Which is cheaper to rent, the RTX 2070 or the V100?▾
Cloud rental prices for both the RTX 2070 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 RTX 2070 have compared to the V100?▾
The RTX 2070 has 8 GB of GDDR6 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX 2070 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 RTX 2070 and the V100?▾
The RTX 2070 uses the Turing architecture (2018) while the V100 uses Volta (2017). The V100 delivers 16.7x the FP16 throughput and 2.0x the memory bandwidth of the RTX 2070.