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 in the V100 deliver a 13x FP16 boost over Pascal's P100, from 9.3 TFLOPS to 125 TFLOPS, enabling faster mixed-precision training for deep learning models. FP32 performance improves by 69 percent, from 9.3 TFLOPS to 15.7 TFLOPS, benefiting single-precision scientific simulations and inference. This delta means V100 accelerates LLM training by handling larger effective batch sizes in FP16-heavy workflows.
Memory bandwidth rises 23 percent from 732 GB/s to 900 GB/s, allowing V100 to process bigger datasets without bottlenecks, supporting batch sizes up to 20-30 percent larger in memory-bound tasks like image generation. The 32 GB VRAM on V100 versus 16 GB on P100 accommodates models exceeding 12 GB, reducing multi-GPU needs. Higher 300W TDP reflects V100's density, yielding 2-5x throughput gains in AI training per benchmarks.
In inference, V100's FP16 prowess cuts latency for real-time serving, while P100 suits FP32-dominant legacy apps. Bandwidth edge aids diffusion models with high data movement.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Tesla 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 |
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 Tesla P100
Choose the P100 for power-constrained environments where 250W TDP fits tighter budgets or older clusters. Its $0.60/hr average pricing across available offers provides value for FP32 workloads at 9.3 TFLOPS matching its FP16 rate. Legacy software tied to Pascal architecture benefits from P100's stability without Volta optimizations.
When to Choose the Tesla V100 32GB
Opt for V100 32GB in modern AI tasks leveraging 125 TF16 TFLOPS for rapid training and 32 GB VRAM for large models. Despite 300W TDP, its 900 GB/s bandwidth and $0.29/hr low-end pricing across 46 offers enable cost-effective scaling. High FP16 suits inference at scale.
Use Cases
V100's 125 TFLOPS FP16 crushes P100's 9.3 TFLOPS for mixed-precision training. 32 GB VRAM handles larger models without splitting.
High FP16 throughput on V100 reduces latency versus P100. Bandwidth at 900 GB/s supports bigger batches.
V100's tensor cores accelerate fine-tuning with 15.7 TFLOPS FP32 and 125 TFLOPS FP16. More VRAM fits adapters.
900 GB/s bandwidth on V100 speeds diffusion steps over P100's 732 GB/s. 32 GB VRAM enables high-res generations.
P100 suffices for FP32 at 9.3 TFLOPS in legacy sims; V100's 15.7 TFLOPS FP32 excels in mixed workloads.
Frequently Asked Questions
Which GPU has more VRAM: P100 or V100?▾
The V100 offers 32 GB HBM2, doubling the P100's 16 GB. This allows V100 to load larger models without multi-GPU setups. Bandwidth also favors V100 at 900 GB/s versus 732 GB/s.
How do FP16 performances compare between P100 and V100?▾
V100 achieves 125 TFLOPS FP16, over 13 times the P100's 9.3 TFLOPS. This boosts mixed-precision AI training significantly. FP32 on V100 is 15.7 TFLOPS versus 9.3 TFLOPS.
What are the cloud prices for P100 vs V100?▾
P100 averages $0.60/hr across one offer. V100 32GB starts at $0.29/hr, averaging $1.01/hr over 46 offers. Deals make V100 often cheaper.
Is V100 faster than P100 for AI training?▾
Yes, V100's 125 TFLOPS FP16 and 900 GB/s bandwidth yield 2-5x speedups over P100. Tensor cores enable this in deep learning. Power is 300W versus 250W.
Do both support NVLink?▾
Both P100 and V100 feature NVLink for multi-GPU scaling. V100 adds PCIe 3.0. Form factors match at SXM2 and PCIe.
When is P100 preferable over V100?▾
P100 fits power-limited setups at 250W TDP and $0.60/hr. It works for Pascal-only software at 9.3 TFLOPS FP32.
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.