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 favors the V100 at 125 TFLOPS, ideal for tensor core-accelerated mixed-precision training that speeds up large model convergence by handling half-precision computations efficiently. The RTX 4070 Ti SUPER trails at 44.1 TFLOPS FP16 but leads in FP32 at 44.1 TFLOPS over the V100's 15.7 TFLOPS, making it superior for FP32-dominant inference, simulations, and graphics where full-precision accuracy matters.
Memory bandwidth and capacity shape real-world use: V100's 900 GB/s and 32 GB enable larger batch sizes in training, minimizing data loading bottlenecks for models exceeding 16 GB. RTX 4070 Ti SUPER's 672 GB/s and 16 GB support optimized modern workflows but constrain massive datasets. Power consumption stands at 285 W for the RTX 4070 Ti SUPER versus 300 W for the V100, with PCIe form factors for both easing cloud integration.
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 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 SUPER
The RTX 4070 Ti SUPER excels in budget-conscious AI inference and fine-tuning. Its 44.1 TFLOPS FP32 performance and $0.09 per hour starting price deliver strong value for tasks fitting within 16 GB VRAM, such as deploying optimized LLMs or image generation.
Gaming-adjacent workloads also benefit from Ada Lovelace features at lower average $0.17 per hour costs.
When to Choose the Tesla V100 32GB
The V100 32GB suits memory-intensive training of large models. Its 32 GB HBM2 and 900 GB/s bandwidth accommodate bigger batches, while 125 TFLOPS FP16 accelerates mixed-precision deep learning.
Legacy datacenter software with NVLink support leverages the V100's SXM2 form factor despite higher $1.01 per hour average pricing.
Use Cases
V100's 32 GB VRAM and 125 TFLOPS FP16 support larger models and batch sizes critical for training. RTX 4070 Ti SUPER's 16 GB limits scale on unoptimized LLMs.
RTX 4070 Ti SUPER's 44.1 TFLOPS FP32 handles full-precision serving efficiently at $0.09 per hour. Lower memory needs make 16 GB sufficient for quantized models.
RTX 4070 Ti SUPER provides 44.1 TFLOPS FP32 for fast iterations on datasets fitting 16 GB VRAM. Cost savings average $0.17 per hour versus V100's $1.01.
Ada Lovelace optimizations and 672 GB/s bandwidth accelerate image generation on RTX 4070 Ti SUPER. Consumer pricing from $0.09 per hour fits creative workflows.
V100's 125 TFLOPS FP16 and 900 GB/s bandwidth excel in HPC simulations requiring high memory throughput. 32 GB HBM2 handles complex datasets.
Frequently Asked Questions
Which GPU has more VRAM: RTX 4070 Ti SUPER or V100 32GB?▾
The V100 32GB offers 32 GB HBM2, doubling the RTX 4070 Ti SUPER's 16 GB GDDR6X. This benefits memory-bound tasks like large-model training. RTX 4070 Ti SUPER suffices for optimized inference.
How do cloud prices compare for RTX 4070 Ti SUPER and V100?▾
RTX 4070 Ti SUPER starts at $0.09 per hour, averaging $0.17 per hour across two offers. V100 32GB begins at $0.29 per hour, averaging $1.01 per hour over 42 offers. Newer GPUs provide better value.
Is RTX 4070 Ti SUPER faster in FP32 than V100?▾
RTX 4070 Ti SUPER delivers 44.1 TFLOPS FP32, surpassing V100's 15.7 TFLOPS. This aids FP32-heavy workloads like simulations. V100 leads in FP16 at 125 TFLOPS.
What is the memory bandwidth difference?▾
V100 achieves 900 GB/s with HBM2, exceeding RTX 4070 Ti SUPER's 672 GB/s GDDR6X. Higher bandwidth supports larger batches on V100. RTX 4070 Ti SUPER balances speed and cost.
Which has lower TDP: RTX 4070 Ti SUPER or V100?▾
RTX 4070 Ti SUPER consumes 285 W, slightly less than V100's 300 W. Both use PCIe form factors in cloud setups. Efficiency favors the newer Ada Lovelace design.
Can RTX 4070 Ti SUPER replace V100 for AI training?▾
RTX 4070 Ti SUPER replaces V100 for models under 16 GB due to 44.1 TFLOPS FP32 and low $0.09 per hour pricing. V100's 32 GB and 125 TFLOPS FP16 remain better for massive training.
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.