Specifications Compared
| Spec | A30 | V100 |
|---|---|---|
| TDP | 165W | 300W |
| VRAM | 24 GB | 16-32 GB |
| CUDA Cores | 3,584 | 5,120 |
| Memory Type | HBM2 | HBM2 |
| Architecture | Ampere | Volta |
| Form Factors | PCIe | SXM2, PCIe |
| Interconnect | NVLink | NVLink, PCIe 3.0 |
| Tensor Cores | 224 | 640 |
| FP16 Performance | 10.3 TFLOPS | 125 TFLOPS |
| FP32 Performance | 10.3 TFLOPS | 15.7 TFLOPS |
| FP64 Performance | 5.2 TFLOPS | 7.8 TFLOPS |
| INT8 Performance | 165 TOPS | |
| Memory Bandwidth | 933 GB/s | 900 GB/s |
Performance Analysis
Compute specifications reveal distinct strengths for real-world workloads. The V100's 125 TFLOPS FP16 performance excels in mixed-precision training, where tensor cores accelerate deep learning iterations far beyond the A30's 10.3 TFLOPS FP16. For FP32 tasks like certain inference or simulations, the V100's 15.7 TFLOPS outperforms the A30's 10.3 TFLOPS, reducing training times in precision-sensitive scenarios.
Memory characteristics influence batch sizes and model scales directly. The A30's 24 GB HBM2 VRAM and 933 GB/s bandwidth support larger batches than the V100's 16 GB and 900 GB/s, minimizing out-of-memory errors in inference or fine-tuning large language models. This bandwidth proximity means workloads scale similarly, but the A30 edges out in sustained data throughput.
Efficiency plays a critical role in deployments. The A30's 165W TDP consumes half the power of the V100's 300W, lowering operational costs in dense cloud racks while maintaining competitive FP32 throughput at 10.3 TFLOPS.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Tesla V100 16GB
| 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 A30
The A30 suits power-sensitive and memory-bound applications. Its 165W TDP enables denser deployments compared to the V100's 300W, ideal for edge or cost-optimized clouds. With 24 GB HBM2 VRAM exceeding the V100's 16 GB, it handles larger models without splitting across GPUs.
Newer Ampere architecture and PCIe form factor simplify integration in modern servers supporting NVLink interconnects.
When to Choose the Tesla V100 16GB
The V100 dominates high-throughput training workloads. Its 125 TFLOPS FP16 crushes the A30's 10.3 TFLOPS, speeding up deep learning iterations significantly. FP32 at 15.7 TFLOPS also surpasses the A30, benefiting scientific computing or legacy pipelines.
Availability drives selection: pricing starts at $0.10 per hour with 25 live offers averaging $0.81 per hour, offering strong value despite 2017 Volta origins.
Use Cases
V100's 125 TFLOPS FP16 accelerates mixed-precision training of large models far beyond A30's 10.3 TFLOPS. Higher FP32 at 15.7 TFLOPS supports stable convergence.
A30's 24 GB VRAM handles larger models than V100's 16 GB, with 933 GB/s bandwidth enabling bigger batches. Balanced 10.3 TFLOPS FP32 suits real-time serving.
V100's 125 TFLOPS FP16 speeds up fine-tuning iterations compared to A30's 10.3 TFLOPS. Low pricing from $0.10 per hour adds economic advantage.
A30's 24 GB VRAM supports higher-resolution generations without issues versus V100's 16 GB. Lower 165W TDP fits creative workflows efficiently.
V100's 15.7 TFLOPS FP32 outperforms A30's 10.3 TFLOPS for simulations. NVLink and PCIe 3.0 interconnects enhance multi-GPU scalability.
Frequently Asked Questions
What is the VRAM difference between A30 and V100 16GB?▾
The A30 offers 24 GB HBM2 VRAM, while the V100 16GB provides 16 GB HBM2. This 8 GB advantage allows the A30 to manage larger models or datasets. Bandwidth is 933 GB/s on A30 versus 900 GB/s on V100.
How do FP16 performances compare?▾
V100 achieves 125 TFLOPS FP16, vastly superior to A30's 10.3 TFLOPS. This gap favors V100 in mixed-precision training tasks. A30 maintains parity with its 10.3 TFLOPS FP32.
What are the TDP ratings?▾
A30 consumes 165W TDP, half of V100's 300W. Lower power on A30 reduces cooling needs in deployments. This efficiency suits dense server configurations.
Is V100 cheaper in the cloud?▾
V100 16GB pricing starts at $0.10 per hour, averaging $0.81 per hour across 25 live offers. A30 currently has no live offers available. V100 provides immediate cost-effective access.
Which has higher FP32 performance?▾
V100 delivers 15.7 TFLOPS FP32 versus A30's 10.3 TFLOPS. This makes V100 better for FP32-dominant workloads like simulations. Architectures differ: Ampere for A30, Volta for V100.
What interconnects do they support?▾
Both feature NVLink, with V100 adding PCIe 3.0. A30 uses PCIe form factor exclusively. These enable multi-GPU scaling in AI clusters.
Which is cheaper to rent, the A30 or the V100?▾
Cloud rental prices for both the A30 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 A30 have compared to the V100?▾
The A30 has 24 GB of HBM2 memory. The V100 has 16 to 32 GB of HBM2 memory.
Can I find A30 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 A30 and the V100?▾
The A30 uses the Ampere architecture (2021) while the V100 uses Volta (2017). The V100 delivers 12.1x the FP16 throughput and 1.0x the memory bandwidth of the A30.