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's superior 125 TFLOPS FP16 performance compared to the RTX 4070's 29.1 TFLOPS makes it preferable for mixed-precision training workloads that leverage tensor cores heavily, accelerating matrix multiplications in deep learning. However, the RTX 4070's equal 29.1 TFLOPS in FP16 and FP32 supports more balanced compute for inference and general-purpose tasks, where FP32 dominance in the V100 at only 15.7 TFLOPS limits versatility.
Memory specifications significantly impact real-world usage: the V100's 32 GB HBM2 and 900 GB/s bandwidth enable larger batch sizes in model training, reducing overhead for datasets exceeding 12 GB, which strains the RTX 4070's GDDR6X. Yet, the RTX 4070's lower 200W TDP versus 300W allows denser cloud deployments, improving cost per TFLOP. For inference, the RTX 4070's modern architecture handles higher throughput per watt despite lower peak specs.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
RTX 4070
| 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
Select the RTX 4070 for cost-sensitive inference and fine-tuning tasks where balanced FP32 performance at 29.1 TFLOPS matches FP16 needs without excess VRAM. Its pricing from $0.07 per hour suits prototyping or small-scale Stable Diffusion runs on 12 GB VRAM. Lower 200W TDP also benefits edge-like cloud setups prioritizing efficiency over raw capacity.
When to Choose the Tesla V100 32GB
Choose the V100 32GB for memory-intensive training of large language models requiring 32 GB HBM2 and 900 GB/s bandwidth to support massive batch sizes. Its 125 TFLOPS FP16 excels in HPC scientific computing or legacy frameworks optimized for Volta. Availability across 44 cloud offers ensures scalability despite higher $1.01 per hour average cost.
Use Cases
V100's 125 TFLOPS FP16 and 32 GB HBM2 with 900 GB/s bandwidth handle large batch sizes for training massive models. RTX 4070's 12 GB VRAM limits scalability.
RTX 4070's balanced 29.1 TFLOPS FP16/FP32 and $0.07 per hour pricing deliver efficient real-time serving. V100's higher 300W TDP increases operational costs.
RTX 4070 suffices for fine-tuning on 12 GB VRAM with 29.1 TFLOPS performance at low $0.14 per hour average. V100 overkill for non-massive datasets.
RTX 4070's Ada architecture optimizes image generation on 12 GB GDDR6X efficiently. Consumer focus aligns with creative workloads versus V100's datacenter design.
V100's NVLink interconnect and 900 GB/s bandwidth support multi-GPU HPC simulations. 32 GB HBM2 handles complex datasets better than RTX 4070.
Frequently Asked Questions
Which GPU has more VRAM?▾
The V100 offers 32 GB HBM2 compared to the RTX 4070's 12 GB GDDR6X. This makes V100 better for memory-bound tasks. RTX 4070 suffices for most inference.
What is the FP16 performance difference?▾
V100 achieves 125 TFLOPS FP16, far exceeding RTX 4070's 29.1 TFLOPS. V100 suits training acceleration. RTX 4070 balances with equal FP32.
How do cloud prices compare?▾
RTX 4070 starts at $0.07 per hour averaging $0.14 across two offers. V100 begins at $0.29 per hour averaging $1.01 across 44 offers. RTX 4070 wins on cost.
Which has higher memory bandwidth?▾
V100 provides 900 GB/s versus RTX 4070's 504 GB/s. Higher bandwidth aids large batch processing on V100. RTX 4070 remains adequate for lighter loads.
What are the power requirements?▾
RTX 4070 has a 200W TDP, lower than V100's 300W. This enables more efficient cloud scaling with RTX 4070. V100 demands robust cooling.
Is RTX 4070 newer than V100?▾
RTX 4070 uses 2023 Ada Lovelace architecture, while V100 is 2017 Volta. Newer design offers RTX 4070 better software support. V100 persists in legacy HPC.
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.