Specifications Compared
| Spec | P100 | V100 |
|---|---|---|
| TDP | 250W | 300W |
| VRAM | 16 GB | 16-32 GB |
| CUDA Cores | 3,584 | 5,120 |
| Memory Type | HBM2 | HBM2 |
| Architecture | Pascal | Volta |
| Form Factors | SXM2, PCIe | SXM2, PCIe |
| Interconnect | NVLink | NVLink, PCIe 3.0 |
| FP16 Performance | 9.3 TFLOPS | 125 TFLOPS |
| FP32 Performance | 9.3 TFLOPS | 15.7 TFLOPS |
| FP64 Performance | 4.7 TFLOPS | 7.8 TFLOPS |
| Memory Bandwidth | 732 GB/s | 900 GB/s |
Performance Analysis
Volta's tensor cores drive V100's FP16 performance to 125 TFLOPS, dwarfing P100's 9.3 TFLOPS. This gap accelerates mixed-precision training by up to 13 times in deep learning. FP32 remains competitive at 15.7 TFLOPS on V100 versus 9.3 TFLOPS on P100, benefiting single-precision simulations.
Higher memory bandwidth on V100, 900 GB/s versus 732 GB/s, supports larger batch sizes in training. For instance, models with high memory demands process data 23% faster. V100's 32 GB option handles bigger datasets than P100's fixed 16 GB, reducing swapping in inference pipelines.
Power draw differs at 300W for V100 and 250W for P100, impacting dense clusters. V100's PCIe 3.0 adds flexibility over P100's NVLink-only scaling.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
P100
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 LeaderGPU | 2×NVIDIA Tesla P100 16GB VRAM | 16GB | 0 vCPU 256GB RAM 960GB Storage | Netherlands | $0.60/GPU/hr $1.20/hr total (2×) | Available |
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 P100
The P100 excels in cost-sensitive environments. At average $0.25 per hour versus V100's $0.94, it fits legacy Pascal-optimized code or low-intensity inference. Its 250W TDP suits power-limited setups, delivering 9.3 TFLOPS FP32 for basic ML tasks without tensor core dependency.
When to Choose the V100
Choose V100 for demanding AI workloads. 125 TFLOPS FP16 enables rapid LLM training, while 900 GB/s bandwidth manages large batches. 32 GB VRAM variants process extensive models unavailable on P100's 16 GB.
Use Cases
V100's 125 TFLOPS FP16 accelerates mixed-precision training far beyond P100's 9.3 TFLOPS. Higher 900 GB/s bandwidth supports large batches essential for LLMs.
V100 handles larger models with up to 32 GB VRAM versus P100's 16 GB. 125 TFLOPS FP16 speeds batched inference.
Tensor cores provide 125 TFLOPS FP16 for efficient fine-tuning, outpacing P100's scalar 9.3 TFLOPS. 900 GB/s bandwidth aids gradient computations.
V100's FP16 dominance at 125 TFLOPS generates images faster than P100's 9.3 TFLOPS. Extra VRAM options prevent out-of-memory errors.
P100's 9.3 TFLOPS FP32 matches many FP32-bound simulations at lower 250W TDP and $0.25 per hour cost.
Frequently Asked Questions
Which GPU has higher FP16 performance?▾
V100 achieves 125 TFLOPS FP16 due to tensor cores. P100 reaches only 9.3 TFLOPS. This makes V100 ideal for modern AI training.
What is the memory bandwidth difference?▾
V100 offers 900 GB/s versus P100's 732 GB/s. The 23% increase allows larger batch sizes in deep learning. Both use HBM2 memory.
How do prices compare in the cloud?▾
P100 starts at $0.07 per hour, averaging $0.25 across 3 offers. V100 starts at $0.10 per hour, averaging $0.94 across 72 offers. V100 has far more availability.
Does V100 support more VRAM?▾
V100 provides 16-32 GB HBM2 options. P100 is limited to 16 GB. This aids memory-intensive inference on V100.
What are the power requirements?▾
P100 draws 250W TDP, lower than V100's 300W. P100 fits power-constrained environments better. Both support NVLink interconnects.
Which is newer?▾
V100 uses 2017 Volta architecture after P100's 2016 Pascal. Volta introduces tensor cores for FP16 gains. Both come in SXM2 and PCIe forms.
Which is cheaper to rent, the P100 or the V100?▾
Cloud rental prices for both the P100 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 P100 have compared to the V100?▾
The P100 has 16 GB of HBM2 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find P100 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 P100 and the V100?▾
The P100 uses the Pascal architecture (2016) while the V100 uses Volta (2017). The V100 delivers 13.4x the FP16 throughput and 1.2x the memory bandwidth of the P100.