Specifications Compared
| Spec | GTX-1070 | V100 |
|---|---|---|
| TDP | 150W | 300W |
| VRAM | 8 GB | 16-32 GB |
| CUDA Cores | 1,920 | 5,120 |
| Memory Type | GDDR5 | HBM2 |
| Architecture | Pascal | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 3.0 | |
| FP16 Performance | 6.5 TFLOPS | 125 TFLOPS |
| FP32 Performance | 6.5 TFLOPS | 15.7 TFLOPS |
| Memory Bandwidth | 256 GB/s | 900 GB/s |
Performance Analysis
Key spec disparities translate directly to workload capabilities. The V100's 125 TFLOPS FP16 versus GTX 1070's 6.5 TFLOPS enables up to 19 times faster mixed-precision training, critical for deep learning where FP16 reduces memory use without accuracy loss. FP32 performance of 15.7 TFLOPS on V100 doubles GTX 1070's 6.5 TFLOPS, benefiting single-precision inference and simulations.
Memory bandwidth defines data handling efficiency: V100's 900 GB/s supports larger batch sizes in training, minimizing bottlenecks for models exceeding 8 GB VRAM. GTX 1070's 256 GB/s limits it to smaller models or batches, risking out-of-memory errors on datasets over 8 GB.
Power and interconnects further differentiate: V100's 300W TDP and NVLink allow multi-GPU scaling for distributed training, absent in GTX 1070's 150W PCIe-only design. These factors make V100 superior for memory-intensive tasks like large language model fine-tuning.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
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 GTX 1070
The GTX 1070 excels in power-constrained or budget local setups. Its 150W TDP fits small desktops or laptops without high cooling needs, ideal for gaming at 1080p or light inference on models under 8 GB VRAM. With no cloud offers, it serves hobbyists buying used hardware cheaply for non-demanding compute like basic Stable Diffusion or scientific visualization.
When to Choose the Tesla V100 32GB
Opt for V100 32GB in professional machine learning environments. The 32 GB HBM2 and 900 GB/s bandwidth handle large models and batches, perfect for LLM training or fine-tuning where GTX 1070's 8 GB GDDR5 fails. Cloud pricing from $0.29 per hour across 46 offers makes it scalable for production inference with NVLink multi-GPU support.
Use Cases
V100's 125 TFLOPS FP16 and 32 GB HBM2 enable efficient training of large models with big batches. GTX 1070's 6.5 TFLOPS and 8 GB VRAM cannot handle the scale.
The 32 GB VRAM and 900 GB/s bandwidth on V100 support high-throughput inference for models over 8 GB. GTX 1070 limits concurrency due to memory constraints.
V100's superior FP16/FP32 ratios and bandwidth accelerate fine-tuning on datasets needing more than 8 GB. GTX 1070 suits only tiny models.
GTX 1070 handles basic image generation under 8 GB VRAM at 6.5 TFLOPS. V100 excels for high-resolution or batched workflows with 32 GB.
V100's 15.7 TFLOPS FP32 and NVLink scaling outperform GTX 1070 in simulations requiring high bandwidth and multi-GPU setups.
Frequently Asked Questions
Can GTX 1070 handle machine learning tasks?▾
GTX 1070 supports basic ML with 6.5 TFLOPS FP32 and 8 GB GDDR5, suitable for small models or inference. It struggles with large datasets due to 256 GB/s bandwidth limits compared to modern GPUs.
How much faster is V100 than GTX 1070 in FP16?▾
V100 delivers 125 TFLOPS FP16, nearly 19 times GTX 1070's 6.5 TFLOPS, accelerating mixed-precision training significantly. This gap shines in deep learning workloads.
What is the VRAM difference between GTX 1070 and V100 32GB?▾
GTX 1070 has 8 GB GDDR5 while V100 offers 32 GB HBM2, allowing four times more model capacity on V100. Bandwidth is 900 GB/s versus 256 GB/s.
Is V100 available on cloud providers?▾
V100 32GB clouds from $0.29 per hour, averaging $1.01 across 46 live offers. GTX 1070 has no current cloud availability.
Which has lower power consumption?▾
GTX 1070 uses 150W TDP, half of V100's 300W, making it better for low-power setups. V100's higher TDP supports greater performance.
Does V100 support multi-GPU?▾
V100 includes NVLink and PCIe 3.0 for interconnects, enabling efficient multi-GPU scaling. GTX 1070 relies solely on PCIe without advanced links.
Which is cheaper to rent, the GTX 1070 or the V100?▾
Cloud rental prices for both the GTX 1070 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 GTX 1070 have compared to the V100?▾
The GTX 1070 has 8 GB of GDDR5 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find GTX 1070 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 GTX 1070 and the V100?▾
The GTX 1070 uses the Pascal architecture (2016) while the V100 uses Volta (2017). The V100 delivers 19.2x the FP16 throughput and 3.5x the memory bandwidth of the GTX 1070.