Specifications Compared
| Spec | RTX-3070 | V100 |
|---|---|---|
| TDP | 220W | 300W |
| VRAM | 8 GB | 16-32 GB |
| CUDA Cores | 5,888 | 5,120 |
| Memory Type | GDDR6 | HBM2 |
| Architecture | Ampere | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| Tensor Cores | 184 | 640 |
| FP16 Performance | 20.3 TFLOPS | 125 TFLOPS |
| FP32 Performance | 20.3 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 448 GB/s | 900 GB/s |
Performance Analysis
FP16 performance disparity proves stark: the V100 achieves 125 TFLOPS, far exceeding the RTX 3070's 20.3 TFLOPS, enabling faster mixed-precision training for deep learning models where tensor cores accelerate computations. This suits large-scale neural network training, reducing epochs significantly. Conversely, FP32 performance favors the RTX 3070 slightly at 20.3 TFLOPS over 15.7 TFLOPS, benefiting single-precision inference or simulations less reliant on half-precision.
Memory bandwidth impacts batch sizes directly: the V100's 900 GB/s supports larger batches in memory-bound tasks like transformer training, minimizing data transfer bottlenecks compared to the RTX 3070's 448 GB/s. The V100's 16 GB HBM2 VRAM handles bigger models without swapping, while the RTX 3070's 8 GB GDDR6 limits it to smaller datasets or quantized inference. Higher TDP of 300 W on the V100 demands robust cooling, whereas 220 W on the RTX 3070 eases deployment in varied cloud instances.
Real-world implications extend to multi-GPU setups: V100 supports NVLink for efficient scaling, absent on the PCIe-only RTX 3070.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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 3070
The RTX 3070 suits cost-sensitive deployments for inference or lightweight training. Its pricing from $0.04 per hour provides value where 8 GB VRAM and 20.3 TFLOPS FP32 suffice for models under 7 billion parameters. Lower 220 W TDP fits edge or small-scale cloud instances without high power costs.
When to Choose the Tesla V100 16GB
Opt for the V100 in memory-intensive training scenarios leveraging 16 GB HBM2 and 900 GB/s bandwidth for large batch sizes. Its 125 TFLOPS FP16 excels in accelerating transformer pretraining, while NVLink enables multi-GPU clusters for distributed workloads unavailable on the RTX 3070.
Use Cases
V100's 125 TFLOPS FP16 and 16 GB HBM2 with 900 GB/s bandwidth handle large-scale training batches efficiently. RTX 3070's 8 GB VRAM limits model sizes.
RTX 3070's 20.3 TFLOPS FP32 and $0.04 per hour pricing support cost-effective serving of models fitting in 8 GB. V100's higher cost averages $0.82 per hour.
RTX 3070 works for small datasets with 20.3 TFLOPS FP32; V100 shines for larger ones via 125 TFLOPS FP16 and more VRAM.
RTX 3070's Ampere architecture and 448 GB/s bandwidth optimize image generation tasks efficiently at low $0.09 per hour average.
V100's 900 GB/s bandwidth and NVLink support high-throughput simulations and multi-GPU scaling better than RTX 3070's PCIe limits.
Frequently Asked Questions
Which GPU has more VRAM: RTX 3070 or V100 16GB?▾
The V100 16GB provides 16 GB HBM2, doubling the RTX 3070's 8 GB GDDR6. This enables larger models on V100 without out-of-memory errors.
RTX 3070 vs V100: which is cheaper in the cloud?▾
RTX 3070 starts at $0.04 per hour with $0.09 average across four offers. V100 begins at $0.10 per hour averaging $0.82 across 27 offers.
Does V100 outperform RTX 3070 in FP16?▾
V100 delivers 125 TFLOPS FP16, over six times the RTX 3070's 20.3 TFLOPS. This accelerates mixed-precision deep learning training significantly.
What is the memory bandwidth difference?▾
V100 offers 900 GB/s, more than double the RTX 3070's 448 GB/s. Higher bandwidth on V100 supports bigger batch sizes in training.
RTX 3070 or V100 for ML inference?▾
RTX 3070 excels with 20.3 TFLOPS FP32 and lower costs for models under 8 GB. V100 suits memory-heavy inference via 16 GB VRAM.
Which has lower power consumption?▾
RTX 3070 uses 220 W TDP versus V100's 300 W. This makes RTX 3070 preferable for power-constrained cloud environments.
Which is cheaper to rent, the RTX 3070 or the V100?▾
Cloud rental prices for both the RTX 3070 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 3070 have compared to the V100?▾
The RTX 3070 has 8 GB of GDDR6 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find RTX 3070 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 3070 and the V100?▾
The RTX 3070 uses the Ampere architecture (2020) while the V100 uses Volta (2017). The V100 delivers 6.2x the FP16 throughput and 2.0x the memory bandwidth of the RTX 3070.