Specifications Compared
| Spec | A100 | P100 |
|---|---|---|
| TDP | 400W | 250W |
| VRAM | 40-80 GB | 16 GB |
| CUDA Cores | 6,912 | 3,584 |
| Memory Type | HBM2e | HBM2 |
| Architecture | Ampere | Pascal |
| Form Factors | SXM4, PCIe | SXM2, PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink |
| Tensor Cores | 432 | |
| FP16 Performance | 312 TFLOPS | 9.3 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 9.3 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 4.7 TFLOPS |
| INT8 Performance | 624 TOPS | |
| Memory Bandwidth | 2,039 GB/s | 732 GB/s |
Performance Analysis
The A100's FP16 performance of 312 TFLOPS dwarfs the P100's 9.3 TFLOPS, accelerating deep learning training where half-precision computations dominate. This delta translates to over 33 times faster matrix multiplications in transformer models, reducing training times from days to hours on large datasets. FP32 performance also improves to 19.5 TFLOPS from 9.3 TFLOPS, benefiting scientific simulations requiring single-precision accuracy.
Memory bandwidth defines practical limits: the A100's 2039 GB/s supports batch sizes up to five times larger than the P100's 732 GB/s, minimizing out-of-memory errors in inference for models like BERT-large. Larger 80 GB VRAM versus 16 GB enables handling full precision checkpoints without sharding, streamlining workflows in multi-GPU setups.
Power efficiency shifts with the A100's 400W TDP versus 250W, but higher throughput per watt favors it for sustained loads. Real-world benchmarks confirm the A100 processes ResNet-50 inference 20 times faster, underscoring its edge in memory-intensive AI tasks.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
A100 PCIe 80GB
| Provider | GPU Model | VRAM | Host Specs | Region | Price | Status | Action | |
|---|---|---|---|---|---|---|---|---|
 Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 63GB RAM 2826GB Storage | Slovenia | $0.73/GPU/hr | Available | ||
Vast.ai | 2×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 256 vCPU 126GB RAM 794GB Storage | Slovenia | $0.73/GPU/hr $1.47/hr total (2×) | Available | ||
 LeaderGPU | 8×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 384GB RAM 2000GB Storage | Netherlands | $0.90/GPU/hr $7.20/hr total (8×) | Available | ||
Vast.ai | NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 64 vCPU 63GB RAM 646GB Storage | Czechia | $1.07/GPU/hr | Available | ||
 Denvr | 8×NVIDIA A100 SXM4 80GB 80GB VRAM | 80GB | 128 vCPU 1024GB RAM 15200GB Storage | Virginia | $1.15/GPU/hr $9.20/hr total (8×) |
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 |
When to Choose the A100 PCIe 80GB
The A100 excels in modern AI workloads demanding high memory capacity. With 80 GB HBM2e VRAM and 2039 GB/s bandwidth, it handles large language model training or Stable Diffusion generation without model parallelism. Cloud users prioritize it for FP16-heavy tasks at 312 TFLOPS, where speed justifies $0.89 to $2.08 per hour pricing.
Inference on batch sizes exceeding 16 GB datasets requires the A100's PCIe 4.0 support and NVLink scalability, outperforming the P100 in production deployments.
When to Choose the Tesla P100
The P100 suits budget-constrained legacy applications. At $0.60 per hour, its 9.3 TFLOPS FP32 performance suffices for classical ML like SVM training or small CNNs fitting within 16 GB HBM2. Lower 250W TDP reduces cooling costs in on-premises or sparse cloud use.
Older HPC codes optimized for Pascal architecture run efficiently on the P100 without recompilation, avoiding the A100's higher power draw.
Use Cases
The A100's 312 TFLOPS FP16 and 80 GB VRAM handle massive datasets and large batch sizes critical for LLM training. The P100's 9.3 TFLOPS and 16 GB limit scalability.
A100's 2039 GB/s bandwidth supports high-throughput inference with large models. P100's 732 GB/s causes bottlenecks for real-time serving.
Fine-tuning benefits from A100's 19.5 TFLOPS FP32 and ample VRAM for parameter-efficient methods. P100 struggles with memory for mid-sized models.
Stable Diffusion requires 80 GB VRAM on A100 for high-resolution generations without swapping. P100's 16 GB restricts image sizes and quality.
P100's 9.3 TFLOPS FP32 fits compute-bound simulations at low cost. A100's superior specs accelerate memory-heavy tasks like molecular dynamics.
Frequently Asked Questions
How much VRAM do the A100 PCIe 80GB and P100 have?▾
The A100 PCIe 80GB features 80 GB HBM2e VRAM. The P100 has 16 GB HBM2. This difference allows the A100 to load larger models without partitioning.
Which GPU has higher FP16 performance?▾
The A100 achieves 312 TFLOPS in FP16. The P100 reaches 9.3 TFLOPS. This makes the A100 over 33 times faster for half-precision AI training.
What is the memory bandwidth comparison?▾
A100 bandwidth is 2039 GB/s. P100 offers 732 GB/s. Higher bandwidth on A100 enables larger batch sizes in deep learning.
What are the current cloud prices?▾
A100 PCIe 80GB starts at $0.89 per hour, averaging $2.08 per hour across 28 offers. P100 is $0.60 per hour across one offer.
Which has higher power consumption?▾
The A100 TDP is 400W. P100 TDP is 250W. A100's higher TDP supports its elevated compute capabilities.
What architectures do they use?▾
A100 uses Ampere from 2020. P100 uses Pascal from 2016. Ampere provides tensor cores absent in Pascal for modern AI acceleration.
Which is cheaper to rent, the A100 or the P100?▾
Cloud rental prices for both the A100 and P100 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 A100 have compared to the P100?▾
The A100 has 40 to 80 GB of HBM2e memory. The P100 has 16 GB of HBM2 memory.
Can I find A100 and P100 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 A100 and the P100?▾
The A100 uses the Ampere architecture (2020) while the P100 uses Pascal (2016). The A100 delivers 33.5x the FP16 throughput and 2.8x the memory bandwidth of the P100.