Specifications Compared
| Spec | RTX-4070 | V100 |
|---|---|---|
| TDP | 200W | 300W |
| VRAM | 12 GB | 16-32 GB |
| CUDA Cores | 5,888 | 5,120 |
| Memory Type | GDDR6X | HBM2 |
| Architecture | Ada Lovelace | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 184 | 640 |
| FP16 Performance | 29.1 TFLOPS | 125 TFLOPS |
| FP32 Performance | 29.1 TFLOPS | 15.7 TFLOPS |
| INT8 Performance | 466 TOPS | |
| Memory Bandwidth | 504 GB/s | 900 GB/s |
Performance Analysis
FP16 performance defines training suitability: the V100's 125 TFLOPS accelerates mixed-precision deep learning iterations, processing large models faster than the RTX 4070 Ti's 29.1 TFLOPS. FP32 balance favors the RTX 4070 Ti at 29.1 TFLOPS over the V100's 15.7 TFLOPS, benefiting inference or single-precision scientific simulations where balanced compute matters. Memory bandwidth directly influences batch sizes: 900 GB/s on the V100 enables larger batches in transformer training by minimizing data loading bottlenecks, whereas 504 GB/s on the RTX 4070 Ti supports moderate batches effectively in optimized Ada kernels. VRAM disparity affects model fitting: 32 GB HBM2 on the V100 accommodates expansive datasets, while 12 GB GDDR6X requires quantization for similar workloads. Efficiency edges the RTX 4070 Ti: its 200W TDP yields better perf-per-watt than the V100's 300W, crucial for scaled cloud deployments.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4070 Ti
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 RunPod | NVIDIA GeForce RTX 4070 Ti 12GB VRAM | 12GB | 6 vCPU 30GB RAM | 🌍global | $0.50/GPU/hr |
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 RTX 4070 Ti
The RTX 4070 Ti excels in cost-sensitive inference and fine-tuning scenarios, leveraging 29.1 TFLOPS FP32 and pricing from $0.08 per hour for high throughput at low overhead. Its 200W TDP and PCIe form factor integrate seamlessly into diverse cloud instances without excessive power demands. Newer Ada architecture optimizations enhance tasks like image generation where 504 GB/s bandwidth and 12 GB VRAM suffice.
When to Choose the Tesla V100 32GB
Opt for the V100 32GB in memory-intensive training workloads, where 32 GB HBM2 and 900 GB/s bandwidth support massive batch sizes unavailable on the RTX 4070 Ti. The 125 TFLOPS FP16 drives rapid mixed-precision computations on large-scale models. NVLink interconnect enables efficient multi-GPU scaling for distributed training.
Use Cases
V100's 32 GB HBM2 VRAM and 125 TFLOPS FP16 handle large language models with bigger batches via 900 GB/s bandwidth.
RTX 4070 Ti's 29.1 TFLOPS FP32 and low $0.08 per hour pricing optimize serving at scale with efficient 200W TDP.
RTX 4070 Ti's balanced 29.1 TFLOPS compute and 12 GB VRAM fit mid-sized models cost-effectively at average $0.22 per hour.
Ada Lovelace architecture on RTX 4070 Ti accelerates diffusion models efficiently with 504 GB/s bandwidth and 200W power draw.
V100's 900 GB/s bandwidth and 32 GB VRAM excel in simulations requiring high data throughput and memory capacity.
Frequently Asked Questions
Which GPU has more VRAM?▾
The V100 32GB offers 32 GB HBM2, doubling the RTX 4070 Ti's 12 GB GDDR6X. This enables larger models on V100. Bandwidth also favors V100 at 900 GB/s over 504 GB/s.
Which is cheaper in the cloud?▾
RTX 4070 Ti rentals start at $0.08 per hour, averaging $0.22 per hour across five offers. V100 32GB begins at $0.29 per hour, averaging $1.01 per hour over 44 offers. RTX 4070 Ti provides better value.
Which performs better in FP16 training?▾
V100 leads with 125 TFLOPS FP16 versus RTX 4070 Ti's 29.1 TFLOPS. This suits mixed-precision deep learning. FP32 reverses: 29.1 TFLOPS on RTX 4070 Ti beats V100's 15.7 TFLOPS.
What are the power requirements?▾
RTX 4070 Ti draws 200W TDP, lower than V100's 300W. This impacts cloud density and costs. PCIe form factor on RTX 4070 Ti offers broader compatibility than V100's SXM2 or PCIe.
Which supports better multi-GPU scaling?▾
V100 includes NVLink and PCIe 3.0 for high-speed interconnects. RTX 4070 Ti relies on PCIe alone. V100 suits distributed training clusters.
How do architectures compare?▾
RTX 4070 Ti uses 2023 Ada Lovelace for modern efficiencies, while V100 employs 2017 Volta. RTX 4070 Ti balances FP32 at 29.1 TFLOPS; V100 specializes in FP16 at 125 TFLOPS.
Which is cheaper to rent, the RTX 4070 or the V100?▾
Cloud rental prices for both the RTX 4070 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 4070 have compared to the V100?▾
The RTX 4070 has 12 GB of GDDR6X memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX 4070 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 4070 and the V100?▾
The RTX 4070 uses the Ada Lovelace architecture (2023) while the V100 uses Volta (2017). The V100 delivers 4.3x the FP16 throughput and 1.8x the memory bandwidth of the RTX 4070.