Specifications Compared
| Spec | RTX-5070 | V100 |
|---|---|---|
| TDP | 250W | 300W |
| VRAM | 12 GB | 16-32 GB |
| CUDA Cores | 6,144 | 5,120 |
| Memory Type | GDDR7 | HBM2 |
| Architecture | Blackwell | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 192 | 640 |
| FP16 Performance | 40.6 TFLOPS | 125 TFLOPS |
| FP32 Performance | 40.6 TFLOPS | 15.7 TFLOPS |
| INT8 Performance | 650 TOPS | |
| Memory Bandwidth | 448 GB/s | 900 GB/s |
Performance Analysis
Memory specifications create distinct capabilities: the V100's 16-32 GB HBM2 and 900 GB/s bandwidth support larger batch sizes in training large models, reducing data transfer bottlenecks compared to the RTX 5070's 12 GB GDDR7 and 448 GB/s. This advantage shines in memory-intensive tasks where exceeding 12 GB triggers swapping or failures on the RTX 5070.
Floating-point performance reveals workload dependencies. The V100's 125 TFLOPS FP16 excels in mixed-precision training, accelerating gradient computations, but its 15.7 TFLOPS FP32 lags behind the RTX 5070's 40.6 TFLOPS FP32, which favors single-precision inference or simulations requiring FP32 dominance. The RTX 5070's balanced tensor cores from Blackwell architecture enhance efficiency in inference pipelines.
Power and form factors influence deployment. The RTX 5070's 250W TDP enables denser cloud packing versus the V100's 300W, while PCIe compatibility simplifies integration over the V100's SXM2 option with NVLink.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
V100
| 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 5070
The RTX 5070 suits cost-sensitive inference and fine-tuning of models under 12 GB VRAM. Its $0.08 per hour starting price and 40.6 TFLOPS FP32 outperform the V100's $0.94 average for single-precision tasks, with 250W TDP reducing operational costs. Newer Blackwell architecture benefits generative AI like Stable Diffusion on PCIe systems.
When to Choose the V100
The V100 excels in heavy training workloads leveraging 125 TFLOPS FP16 and up to 32 GB HBM2. High 900 GB/s bandwidth supports large-batch LLM training, where the RTX 5070's 448 GB/s and 12 GB limit scale. NVLink interconnect aids multi-GPU setups despite higher $0.94 average pricing.
Use Cases
V100's 125 TFLOPS FP16 and 16-32 GB HBM2 handle large-batch training effectively. RTX 5070's 12 GB VRAM and 40.6 TFLOPS FP16 constrain scale for massive LLMs.
RTX 5070's 40.6 TFLOPS FP32 surpasses V100's 15.7 TFLOPS for single-precision serving. Lower $0.17 average pricing supports high-volume deployments.
V100's 900 GB/s bandwidth and up to 32 GB VRAM accommodate larger datasets during fine-tuning. RTX 5070 suits only smaller models under 12 GB.
RTX 5070's Blackwell architecture and 448 GB/s GDDR7 optimize image generation pipelines. Cost at $0.08 per hour beats V100 for creative workloads.
V100's 125 TFLOPS FP16 accelerates HPC simulations with high memory needs up to 32 GB. NVLink enhances multi-node performance over RTX 5070's PCIe.
Frequently Asked Questions
Which GPU has more VRAM?▾
The V100 offers 16-32 GB HBM2, exceeding the RTX 5070's 12 GB GDDR7. This makes V100 preferable for models exceeding 12 GB. RTX 5070 suffices for smaller workloads.
How do FP16 performances compare?▾
V100 delivers 125 TFLOPS FP16, far above RTX 5070's 40.6 TFLOPS. V100 accelerates mixed-precision training significantly. RTX 5070 balances with equal FP32.
What is the price difference in cloud?▾
RTX 5070 starts at $0.08 per hour averaging $0.17 across four offers, versus V100's $0.10 starting and $0.94 average across 72. RTX 5070 provides better value for cost-sensitive users.
Which has higher memory bandwidth?▾
V100 achieves 900 GB/s with HBM2, doubling RTX 5070's 448 GB/s GDDR7. Higher bandwidth on V100 supports larger batches in training. RTX 5070 handles inference adequately.
Compare their power consumption.▾
RTX 5070 uses 250W TDP, lower than V100's 300W. This enables more efficient cloud density for RTX 5070. V100's higher draw suits dedicated datacenter racks.
Is RTX 5070 newer than V100?▾
RTX 5070 uses 2025 Blackwell architecture, versus V100's 2017 Volta. Newer design brings tensor core improvements to RTX 5070. V100 remains viable for legacy high-VRAM needs.
Which is cheaper to rent, the RTX 5070 or the V100?▾
Cloud rental prices for both the RTX 5070 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 5070 have compared to the V100?▾
The RTX 5070 has 12 GB of GDDR7 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX 5070 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 5070 and the V100?▾
The RTX 5070 uses the Blackwell architecture (2025) while the V100 uses Volta (2017). The V100 delivers 3.1x the FP16 throughput and 2.0x the memory bandwidth of the RTX 5070.