NVIDIA A30 VS AMD Radeon Pro V520
Choosing between **A30** and **Radeon Pro V520** depends on your specific AI workload requirements. While the **A30** offers more VRAM for larger models, the **Radeon Pro V520** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.11/h** and **$0.19/h** respectively across 7 providers.
Radeon Pro V520
📊 Detailed Specifications Comparison
| Specification | A30 | Radeon Pro V520 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Ampere | RDNA 1 | - |
| Process Node | 7nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Dual-slot PCIe | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 24GB | 8GB | +200% |
| Memory Type | HBM2 | HBM2 | - |
| Memory Bandwidth | 933 GB/s | 512 GB/s | +82% |
| Memory Bus Width | 3072-bit | 2048-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 3,584 | N/A | |
| Tensor Cores (AI) | 224 | N/A | |
| Stream Processors | N/A | 2,304 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 5.2 TFLOPS | 9.4 TFLOPS | -45% |
| FP16 (Half Precision) | 165 TFLOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 165W | 225W | -27% |
| PCIe Interface | PCIe 4.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA A30
Higher VRAM capacity and memory bandwidth are critical for training large language models. The A30 offers 24GB compared to 8GB.
AI Inference
NVIDIA A30
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A30
Based on current cloud pricing, the A30 starts at a lower hourly rate.
Technical Deep Dive: A30 vs Radeon Pro V520
This head-to-head pits NVIDIA's Ampere against AMD's RDNA 1. The A30 has a significant **16GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **A30** is currently about **42% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA A30 is Best For:
- Enterprise AI inference
- Mainstream compute
- Heavy model training
AMD Radeon Pro V520 is Best For:
- Cloud gaming
- Virtualization
- AI training
Frequently Asked Questions
Which GPU is better for AI training: A30 or Radeon Pro V520?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The A30 offers 24GB of HBM2 memory with 933 GB/s bandwidth, while the Radeon Pro V520 provides 8GB of HBM2 with 512 GB/s bandwidth. For larger models, the A30's higher VRAM capacity gives it an advantage.
What is the price difference between A30 and Radeon Pro V520 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, A30 starts at $0.11/hour while Radeon Pro V520 starts at $0.19/hour. This represents a 42% price difference.
Can I use Radeon Pro V520 instead of A30 for my workload?
It depends on your specific requirements. If your model fits within 8GB of VRAM and you don't need the additional throughput of the A30, the Radeon Pro V520 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the A30's architecture may be essential.
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