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
The V100's FP16 throughput of 125 TFLOPS vastly outpaces the GTX 1070's 6.5 TFLOPS, enabling 19 times faster mixed-precision training in deep learning models. This delta accelerates gradient computations in frameworks like PyTorch, reducing epochs from days to hours on large datasets. FP32 performance shows the V100 at 15.7 TFLOPS versus 6.5 TFLOPS, a 2.4 times improvement for single-precision inference and simulations.
Memory bandwidth defines workload scalability: the V100's 900 GB/s supports batch sizes up to 4 times larger than the GTX 1070's 256 GB/s limit, minimizing data starvation in transformer models. Higher VRAM on the V100, 16-32 GB versus 8 GB, accommodates models exceeding 7 billion parameters without swapping, crucial for LLM fine-tuning. These specs translate to real-world gains: V100 handles ResNet-50 inference at 10 times the throughput of GTX 1070 in MLPerf benchmarks.
Power efficiency reveals trade-offs. The GTX 1070's 150W TDP yields 43 GFLOPS per watt in FP32, edging the V100's 52 GFLOPS per watt at 300W, but absolute performance dominates AI pipelines.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
V100
| 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 suits legacy gaming setups or lightweight graphics tasks where 6.5 TFLOPS FP32 suffices for 1080p rendering. Its 150W TDP enables deployment in power-constrained desktops without specialized cooling. With PCIe form factor and no current cloud offers, it appeals to on-premise hobbyists avoiding V100's $0.94 per hour average.
When to Choose the V100
The V100 excels in AI training and HPC where 125 TFLOPS FP16 drives rapid iteration on large models. NVLink interconnect and 900 GB/s bandwidth optimize multi-GPU scaling for distributed workloads. Cloud availability across 72 offers from $0.10 per hour makes it ideal for bursty professional use.
Use Cases
V100's 125 TFLOPS FP16 accelerates mixed-precision training 19 times over GTX 1070's 6.5 TFLOPS. 16-32 GB HBM2 supports large batch sizes without OOM errors.
900 GB/s bandwidth on V100 enables high-throughput serving versus GTX 1070's 256 GB/s bottleneck. FP32 at 15.7 TFLOPS doubles GTX 1070's 6.5 TFLOPS for real-time queries.
V100's tensor cores and 125 TFLOPS FP16 speed gradient updates 19-fold compared to GTX 1070. NVLink aids multi-GPU fine-tuning scalability.
V100's 16-32 GB VRAM fits full models without tiling, unlike GTX 1070's 8 GB limit. 900 GB/s bandwidth generates images 3.5 times faster.
15.7 TFLOPS FP32 and PCIe 3.0/NVLink on V100 outperform GTX 1070's 6.5 TFLOPS for simulations. Higher bandwidth handles large matrices efficiently.
Frequently Asked Questions
How much faster is V100 than GTX 1070 in FP16?▾
V100 delivers 125 TFLOPS FP16 versus GTX 1070's 6.5 TFLOPS, a 19 times performance advantage. This gap shines in deep learning training with mixed precision.
Can GTX 1070 handle modern AI workloads?▾
GTX 1070's 8 GB VRAM and 256 GB/s bandwidth limit it to small models under 1 billion parameters. It struggles with batch sizes beyond 16 on transformers due to memory constraints.
What is the cloud pricing for V100?▾
V100 offers start at $0.10 per hour, averaging $0.94 per hour across 72 providers. GTX 1070 has no live cloud offers.
Which has better memory bandwidth?▾
V100 provides 900 GB/s with HBM2, 3.5 times the GTX 1070's 256 GB/s GDDR5. This enables larger batches in training.
Is V100 more power-hungry?▾
V100's 300W TDP doubles GTX 1070's 150W, but yields 52 GFLOPS per watt FP32 versus 43. Absolute output favors V100 for compute-intensive tasks.
What interconnects does V100 support?▾
V100 uses NVLink and PCIe 3.0 for multi-GPU scaling, absent on GTX 1070's PCIe-only design. This boosts distributed training efficiency.
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.