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 performance differences shape real-world applications profoundly. The V100's 125 TFLOPS FP16 capability, far exceeding A30's 10.3 TFLOPS, accelerates mixed-precision training in deep learning models, where FP16 reduces memory usage and speeds iterations by leveraging tensor cores. A30's equal 10.3 TFLOPS FP16 and FP32 rates favor FP32-heavy inference or simulations less reliant on low-precision acceleration.
Memory bandwidth impacts batch processing: A30's 933 GB/s enables marginally larger batches than V100's 900 GB/s in memory-intensive inference, sustaining higher throughput for models like transformers. V100's 32 GB VRAM supports bigger models or batches outright compared to A30's 24 GB.
Efficiency stands out with A30's 165W TDP versus V100's 300W, allowing greater GPU density in servers and lower operational costs. Both support NVLink interconnects, but A30's PCIe form factor simplifies single-node deployments.
Live Cloud Pricing
Real-time prices from 25+ providers. Updated every 60 seconds.
Tesla V100 32GB
| 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 proves superior in power-constrained or dense computing setups. Its 165W TDP consumes half the power of V100's 300W, enabling servers to host more GPUs without exceeding cooling limits.
Newer Ampere architecture from 2021 ensures compatibility with modern CUDA versions and optimized libraries, benefiting ongoing AI development over V100's 2017 Volta base.
When to Choose the Tesla V100 32GB
The V100 excels in high-compute training scenarios. Its 125 TFLOPS FP16 performance outperforms A30's 10.3 TFLOPS, ideal for mixed-precision deep learning workloads.
With 32 GB VRAM and pricing from $0.29/hr across 46 offers, it handles large models cost-effectively, surpassing A30's 24 GB capacity where no live pricing exists.
Use Cases
V100's 125 TFLOPS FP16 accelerates mixed-precision training far beyond A30's 10.3 TFLOPS. Its 32 GB VRAM supports larger models during extended training runs.
V100's 32 GB VRAM handles bigger batch sizes for LLMs compared to A30's 24 GB. Pricing from $0.29/hr makes it economical for high-throughput serving.
High 125 TFLOPS FP16 on V100 speeds fine-tuning iterations in low-precision modes. 32 GB capacity accommodates substantial parameter sets over A30's limits.
V100's 32 GB VRAM fits larger diffusion models without swapping, unlike A30's 24 GB. FP16 performance at 125 TFLOPS boosts generation speeds.
A30's balanced 10.3 TFLOPS FP32 matches its FP16 for FP32-dominant simulations. Lower 165W TDP suits sustained scientific workloads efficiently.
Frequently Asked Questions
Which GPU has more VRAM: A30 or V100?▾
The V100 provides 32 GB HBM2, exceeding A30's 24 GB HBM2. This advantage supports larger models in memory-intensive tasks like LLM inference.
Does A30 or V100 have higher FP16 performance?▾
V100 achieves 125 TFLOPS FP16, vastly superior to A30's 10.3 TFLOPS. This gap benefits mixed-precision training workloads significantly.
What is the power consumption difference between A30 and V100?▾
A30 draws 165W TDP, half of V100's 300W TDP. Lower power on A30 allows higher density in cloud servers.
Is V100 cheaper in the cloud than A30?▾
V100 32GB starts at $0.29/hr with average $1.01/hr across 46 offers. A30 currently has no live cloud offers available.
Which has better memory bandwidth: A30 or V100?▾
A30 offers 933 GB/s bandwidth over V100's 900 GB/s. This supports slightly larger batches in bandwidth-limited scenarios.
Do both A30 and V100 support NVLink?▾
Both GPUs include NVLink interconnect support. V100 also features PCIe 3.0, while A30 uses PCIe form factor.
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.