Specifications Compared
| Spec | A30 | P100 |
|---|---|---|
| TDP | 165W | 250W |
| VRAM | 24 GB | 16 GB |
| CUDA Cores | 3,584 | 3,584 |
| Memory Type | HBM2 | HBM2 |
| Architecture | Ampere | Pascal |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink | NVLink |
| Tensor Cores | 224 | |
| FP16 Performance | 10.3 TFLOPS | 9.3 TFLOPS |
| FP32 Performance | 10.3 TFLOPS | 9.3 TFLOPS |
| FP64 Performance | 5.2 TFLOPS | 4.7 TFLOPS |
| INT8 Performance | 165 TOPS | |
| Memory Bandwidth | 933 GB/s | 732 GB/s |
Performance Analysis
The A30's 24 GB VRAM versus the P100's 16 GB directly impacts real-world workloads: larger VRAM accommodates bigger batch sizes in model training, reducing the need for gradient accumulation. This proves essential for deep learning tasks where memory constraints limit throughput on the P100.
Memory bandwidth differences are significant: 933 GB/s on the A30 compared to 732 GB/s on the P100 accelerates data movement, benefiting memory-bound operations like inference serving. Higher bandwidth minimizes bottlenecks in scenarios with frequent memory access, such as processing large datasets.
FP16 and FP32 performance at 10.3 TFLOPS on the A30 exceeds the P100's 9.3 TFLOPS, offering about 11 percent more compute capacity. This edge supports faster mixed-precision training and inference, where FP16 dominates for efficiency. The A30's lower 165W TDP versus 250W also enhances density in cloud deployments, allowing more GPUs per server without excessive cooling demands.
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 |
When to Choose the A30
The A30 suits modern AI workloads requiring substantial memory: its 24 GB VRAM handles larger language models that exceed the P100's 16 GB limit. Ampere architecture from 2021 provides optimized tensor cores absent in Pascal, improving efficiency for training and inference.
Power-conscious environments favor the A30's 165W TDP over 250W, enabling higher GPU density in PCIe form factor setups.
When to Choose the P100
Budget-limited projects benefit from the P100's cloud pricing starting at $0.07 per hour, averaging $0.25 per hour across three offers, while the A30 lacks live availability. Legacy Pascal-compatible codebases run seamlessly on the P100 without recompilation needs.
Workloads fitting within 16 GB VRAM, such as smaller-scale scientific simulations, leverage the P100's SXM2 and PCIe form factors effectively.
Use Cases
The A30's 24 GB VRAM supports larger models and batch sizes compared to the P100's 16 GB. Higher 10.3 TFLOPS FP16 performance accelerates training iterations.
933 GB/s bandwidth on the A30 enables faster token generation than the P100's 732 GB/s. 24 GB VRAM fits bigger models for high-throughput serving.
Ampere architecture and 10.3 TFLOPS FP32 on the A30 speed up fine-tuning over Pascal's 9.3 TFLOPS. Extra 8 GB VRAM handles parameter-efficient methods.
A30's newer Ampere features optimize diffusion models with 24 GB VRAM for high-resolution generations. Superior bandwidth reduces latency versus P100.
P100's availability at $0.07 per hour suits cost-sensitive HPC tasks within 16 GB VRAM. NVLink interconnect matches A30 for multi-GPU simulations.
Frequently Asked Questions
What is the VRAM difference between A30 and P100?▾
The A30 provides 24 GB HBM2 VRAM, while the P100 offers 16 GB HBM2. This 8 GB advantage allows the A30 to manage larger datasets or models. Both use high-bandwidth memory for AI tasks.
How do FP32 performance levels compare?▾
A30 delivers 10.3 TFLOPS FP32, exceeding P100's 9.3 TFLOPS by about 11 percent. This translates to faster single-precision computations in training. FP16 matches this delta at identical rates per GPU.
Which GPU is more power efficient?▾
A30 consumes 165W TDP, lower than P100's 250W. This enables denser deployments in cloud servers. Efficiency gains stem from Ampere architecture advancements.
What are the current cloud prices for these GPUs?▾
P100 starts at $0.07 per hour, averaging $0.25 per hour across three live offers. A30 has no live offers currently available. Prices reflect P100's older Pascal status.
Do both support NVLink?▾
Yes, both A30 and P100 feature NVLink interconnects for multi-GPU scaling. This facilitates high-speed communication in clusters. Form factors differ: A30 is PCIe only, P100 offers SXM2 and PCIe.
Which has higher memory bandwidth?▾
A30 achieves 933 GB/s bandwidth versus P100's 732 GB/s. The 27 percent increase benefits memory-intensive workloads like inference. HBM2 memory type remains consistent.
Which is cheaper to rent, the A30 or the P100?▾
Cloud rental prices for both the A30 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 A30 have compared to the P100?▾
The A30 has 24 GB of HBM2 memory. The P100 has 16 GB of HBM2 memory.
Can I find A30 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 A30 and the P100?▾
The A30 uses the Ampere architecture (2021) while the P100 uses Pascal (2016). The A30 delivers 1.1x the FP16 throughput and 1.3x the memory bandwidth of the P100.