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
V100 demonstrates superior peak performance: its 125 TFLOPS FP16 capability significantly exceeds A30's 10.3 TFLOPS, accelerating mixed-precision deep learning training where tensor core operations dominate. FP32 performance at 15.7 TFLOPS on V100 outpaces A30's 10.3 TFLOPS, benefiting simulations and graphics rendering that rely on single-precision arithmetic.
Memory bandwidth shows A30 with a slight edge at 933 GB/s over V100's 900 GB/s: this enables marginally larger batch sizes in memory-bound inference tasks, reducing data transfer bottlenecks. However, V100's variable 16-32 GB VRAM capacity supports larger models compared to A30's fixed 24 GB, crucial for handling extensive datasets without swapping.
Power efficiency favors A30 at 165W TDP versus V100's 300W: lower consumption suits dense server configurations, potentially lowering operational costs despite V100's higher throughput in compute-intensive scenarios.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
V100
| 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
Opt for A30 in power-sensitive deployments: its 165W TDP allows twice the GPU density compared to V100's 300W, optimizing rack space and cooling in large-scale clusters. Ampere architecture from 2021 ensures compatibility with modern CUDA versions and frameworks like TensorRT 8+.
When to Choose the V100
V100 suits high-throughput workloads: 125 TFLOPS FP16 and 15.7 TFLOPS FP32 deliver faster training times for neural networks versus A30's 10.3 TFLOPS rates. Cloud availability from $0.10 per hour across 72 offers provides economical access for bursty compute needs.
Use Cases
V100's 125 TFLOPS FP16 accelerates mixed-precision training of large language models far beyond A30's 10.3 TFLOPS.
Higher FP32 at 15.7 TFLOPS on V100 supports faster inference batches; up to 32 GB VRAM handles bigger models than A30's 24 GB.
Both offer NVLink and ample HBM2; choose V100 for speed via 125 TFLOPS FP16 or A30 for 165W efficiency.
V100's superior 15.7 TFLOPS FP32 excels in diffusion model generation compared to A30's 10.3 TFLOPS.
A30's lower 165W TDP and 933 GB/s bandwidth fit sustained simulations better than V100's 300W draw.
Frequently Asked Questions
Which GPU has higher FP16 performance?▾
V100 achieves 125 TFLOPS FP16, vastly surpassing A30's 10.3 TFLOPS. This gap favors V100 in tensor-heavy deep learning tasks.
What is the VRAM difference between A30 and V100?▾
A30 provides 24 GB HBM2 fixed, while V100 ranges from 16-32 GB HBM2. V100 suits larger models with its maximum capacity.
How do power consumptions compare?▾
A30 consumes 165W TDP, half of V100's 300W. A30 enables denser deployments in power-limited data centers.
Is A30 or V100 better for cloud pricing?▾
V100 has live offers from $0.10 per hour, averaging $0.94 per hour across 72 providers. A30 lacks current listings.
Which has higher memory bandwidth?▾
A30 reaches 933 GB/s, slightly above V100's 900 GB/s. This aids A30 in bandwidth-constrained batch processing.
What architectures do they use?▾
A30 uses Ampere from 2021 for modern optimizations. V100 employs Volta from 2017 with strong legacy tensor core performance.
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.