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
The V100 16GB dominates in FP16 workloads at 125 TFLOPS versus 29.1 TFLOPS on the RTX 4070 Ti SUPER: this enables faster deep learning training where mixed-precision techniques reduce memory usage and accelerate iterations. Inference tasks benefit less from FP16 peaks, favoring the RTX 4070 Ti SUPER's superior FP32 at 29.1 TFLOPS over 15.7 TFLOPS for precise single-precision computations. Memory bandwidth creates a clear divide: V100's 900 GB/s supports larger batch sizes in memory-bound scenarios like transformer training, minimizing data starvation. RTX 4070 Ti SUPER's 504 GB/s suffices for smaller batches or inference. VRAM difference matters too: 16 GB on V100 handles bigger models without swapping, while 12 GB limits RTX 4070 Ti SUPER in VRAM-intensive cases. Form factors reflect use: PCIe-only RTX 4070 Ti SUPER suits general clouds, while V100's NVLink and SXM2 excel in multi-GPU clusters.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4070 Ti SUPER
| 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 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 4070 Ti SUPER
The RTX 4070 Ti SUPER suits cost-sensitive deployments with its $0.09 per hour starting price and 200W TDP for efficient cooling. It excels in FP32-dominant inference or fine-tuning where 29.1 TFLOPS outperforms V100's 15.7 TFLOPS. Modern Ada Lovelace features enhance ray tracing or hybrid gaming-compute tasks on PCIe systems.
When to Choose the Tesla V100 16GB
Choose V100 16GB for FP16-heavy training workloads leveraging 125 TFLOPS and 900 GB/s bandwidth for large-batch processing. Its 16 GB HBM2 VRAM fits expansive models, and NVLink interconnect scales multi-GPU setups unavailable on RTX 4070 Ti SUPER. Legacy datacenter optimization persists despite higher $0.82 per hour average.
Use Cases
V100 16GB's 125 TFLOPS FP16 and 900 GB/s bandwidth enable faster training of large language models with bigger batches than RTX 4070 Ti SUPER's 29.1 TFLOPS and 504 GB/s.
RTX 4070 Ti SUPER's 29.1 TFLOPS FP32 outperforms V100's 15.7 TFLOPS for precise inference, with lower $0.17 per hour cost suiting high-throughput serving.
Balanced FP32/FP16 at 29.1 TFLOPS and cheaper pricing make RTX 4070 Ti SUPER ideal for iterative fine-tuning, avoiding V100's higher power and cost.
RTX 4070 Ti SUPER's Ada architecture optimizes image generation tasks with 12 GB VRAM sufficient for most Stable Diffusion models at lower 200W TDP.
V100 16GB's 125 TFLOPS FP16 accelerates simulations in HPC, with 16 GB HBM2 and NVLink outperforming RTX 4070 Ti SUPER in clustered scientific workloads.
Frequently Asked Questions
Which GPU has more VRAM?▾
The V100 16GB provides 16 GB HBM2, exceeding the RTX 4070 Ti SUPER's 12 GB GDDR6X. This allows V100 to load larger models without offloading.
What is the FP16 performance difference?▾
V100 16GB delivers 125 TFLOPS FP16, over four times the RTX 4070 Ti SUPER's 29.1 TFLOPS. This gap favors V100 in half-precision training.
Which is cheaper in the cloud?▾
RTX 4070 Ti SUPER starts at $0.09 per hour averaging $0.17 per hour across two offers, versus V100 16GB at $0.10 per hour averaging $0.82 per hour across 27 offers.
How do memory bandwidths compare?▾
V100 16GB offers 900 GB/s, nearly double the RTX 4070 Ti SUPER's 504 GB/s. Higher bandwidth on V100 supports larger batch sizes in data-heavy tasks.
Which has lower power consumption?▾
RTX 4070 Ti SUPER uses 200W TDP, lower than V100 16GB's 300W. This reduces cooling needs in dense cloud deployments.
Is V100 better for multi-GPU setups?▾
Yes, V100 16GB supports NVLink and SXM2 for high-speed interconnects, unlike PCIe-only RTX 4070 Ti SUPER.
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.