Specifications Compared
| Spec | A100 | A30 |
|---|---|---|
| TDP | 400W | 165W |
| VRAM | 40-80 GB | 24 GB |
| CUDA Cores | 6,912 | 3,584 |
| Memory Type | HBM2e | HBM2 |
| Architecture | Ampere | Ampere |
| Form Factors | SXM4, PCIe | PCIe |
| Interconnect | NVLink, PCIe 4.0, InfiniBand | NVLink |
| Tensor Cores | 432 | 224 |
| FP16 Performance | 312 TFLOPS | 10.3 TFLOPS |
| FP32 Performance | 19.5 TFLOPS | 10.3 TFLOPS |
| FP64 Performance | 9.7 TFLOPS | 5.2 TFLOPS |
| INT8 Performance | 624 TOPS | 165 TOPS |
| Memory Bandwidth | 2,039 GB/s | 933 GB/s |
Performance Analysis
The A100 PCIe 80GB dominates in compute performance: its 312 TFLOPS FP16 rate accelerates mixed-precision training by over 30 times compared to the A30's 10.3 TFLOPS, allowing faster convergence on large neural networks. The A100's 19.5 TFLOPS FP32 also surpasses the A30's 10.3 TFLOPS, benefiting single-precision scientific simulations or legacy code.
The A30's equal 10.3 TFLOPS across FP16 and FP32 suits inference tasks where balanced precision avoids overheads from format conversions. Memory bandwidth reveals a key gap: the A100's 2039 GB/s supports larger batch sizes in training, reducing per-iteration latency, while the A30's 933 GB/s limits it to smaller batches and may increase memory swapping in VRAM-constrained scenarios.
Power efficiency favors the A30 at 165W TDP, enabling higher density in servers, but the A100's 80 GB HBM2e VRAM handles models that exceed the A30's 24 GB capacity, preventing out-of-memory errors in real-world deployments.
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 557GB Storage | Czechia | $1.00/GPU/hr | Available | ||
 Denvr | 4×NVIDIA A100 PCIe 80GB 80GB VRAM | 80GB | 64 vCPU 512GB RAM 7600GB Storage | Virginia | $1.15/GPU/hr $4.60/hr total (4×) |
When to Choose the A100 PCIe 80GB
Choose the NVIDIA A100 PCIe 80GB for memory-intensive AI training where 80 GB HBM2e VRAM and 2039 GB/s bandwidth enable large batch sizes without fragmentation. Its 312 TFLOPS FP16 performance excels in LLM pretraining or fine-tuning massive transformers, reducing epochs from days to hours compared to lower-spec alternatives.
Multi-node scaling via NVLink, PCIe 4.0, and InfiniBand makes it ideal for HPC clusters handling datasets over 40 GB per GPU.
When to Choose the A30
Select the NVIDIA A30 for power-constrained inference servers: its 165W TDP allows denser deployments than the A100's 400W, cutting cooling costs in edge data centers. The balanced 10.3 TFLOPS FP16 and FP32 rates optimize real-time serving of models fitting within 24 GB HBM2.
PCIe form factor simplifies integration into standard racks for enterprise VDI or moderate-scale inference without NVLink complexity.
Use Cases
The A100's 312 TFLOPS FP16 and 80 GB HBM2e VRAM support massive batch sizes for efficient training of billion-parameter models. The A30's 10.3 TFLOPS and 24 GB limit scalability.
A100's 2039 GB/s bandwidth and higher FP16 throughput enable low-latency serving of large LLMs at scale. A30 suits smaller models but bottlenecks on memory-intensive prompts.
80 GB VRAM on A100 accommodates full model loading for parameter-efficient fine-tuning without sharding. A30's 24 GB requires gradient checkpointing, slowing iterations.
A100's 312 TFLOPS FP16 accelerates diffusion sampling with high-resolution images fitting in 80 GB VRAM. A30 struggles with memory for complex generations.
A100 excels in FP32-heavy simulations at 19.5 TFLOPS with InfiniBand scaling. A30's 165W efficiency fits power-limited HPC nodes for FP16-optimized codes.
Frequently Asked Questions
What is the VRAM difference between A100 PCIe 80GB and A30?▾
The A100 PCIe 80GB offers 80 GB HBM2e VRAM, while the A30 has 24 GB HBM2. This gap allows the A100 to load larger models without offloading to host memory.
How do FP16 performance rates compare?▾
A100 achieves 312 TFLOPS FP16, over 30 times the A30's 10.3 TFLOPS. This makes A100 ideal for accelerated mixed-precision training.
What are the power consumption levels?▾
The A100 has a 400W TDP, compared to the A30's 165W. Lower TDP on A30 enables more GPUs per server rack.
Is cloud pricing available for these GPUs?▾
A100 PCIe 80GB pricing starts at $0.89 per hour, averaging $2.08 per hour across 28 offers. No live offers exist for A30.
What interconnects do they support?▾
Both offer NVLink and PCIe, but A100 adds PCIe 4.0 and InfiniBand for faster multi-GPU communication. A30 is PCIe-only.
Which has higher memory bandwidth?▾
A100 provides 2039 GB/s, more than double the A30's 933 GB/s. Higher bandwidth on A100 supports larger training batches.
Which is cheaper to rent, the A100 or the A30?▾
Cloud rental prices for both the A100 and A30 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 A30?▾
The A100 has 40 to 80 GB of HBM2e memory. The A30 has 24 GB of HBM2 memory.
Can I find A100 and A30 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 A30?▾
The A100 uses the Ampere architecture (2020) while the A30 uses Ampere (2021). The A100 delivers 30.3x the FP16 throughput and 2.2x the memory bandwidth of the A30.