NVIDIA V100 VS AMD Radeon Pro V520
Choosing between **V100** and **Radeon Pro V520** depends on your specific AI workload requirements. The **V100** leads in both memory capacity and raw compute power, making it a stronger choice for high-end LLM training. Currently, you can rent these GPUs starting from **$0.13/h** and **$0.19/h** respectively across 18 providers.
Radeon Pro V520
📊 Detailed Specifications Comparison
| Specification | V100 | Radeon Pro V520 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Volta | RDNA 1 | - |
| Process Node | 12nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM2 / PCIe | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 32GB | 8GB | +300% |
| Memory Type | HBM2 | HBM2 | - |
| Memory Bandwidth | 900 GB/s | 512 GB/s | +76% |
| Memory Bus Width | 4096-bit | 2048-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 5,120 | N/A | |
| Tensor Cores (AI) | 640 | N/A | |
| Stream Processors | N/A | 2,304 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 15.7 TFLOPS | 9.4 TFLOPS | +67% |
| FP16 (Half Precision) | 125 TFLOPS | N/A | |
| FP64 (Double Precision) | 7.8 TFLOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 300W | 225W | +33% |
| PCIe Interface | PCIe 3.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA V100
Higher VRAM capacity and memory bandwidth are critical for training large language models. The V100 offers 32GB compared to 8GB.
AI Inference
NVIDIA V100
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA V100
Based on current cloud pricing, the V100 starts at a lower hourly rate.
Technical Deep Dive: V100 vs Radeon Pro V520
This head-to-head pits NVIDIA's Volta against AMD's RDNA 1. The V100 has a significant **24GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **V100** is currently about **32% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA V100 is Best For:
- Deep learning training
- Scientific research
- Latest generation workloads
AMD Radeon Pro V520 is Best For:
- Cloud gaming
- Virtualization
- AI training
Frequently Asked Questions
Which GPU is better for AI training: V100 or Radeon Pro V520?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The V100 offers 32GB of HBM2 memory with 900 GB/s bandwidth, while the Radeon Pro V520 provides 8GB of HBM2 with 512 GB/s bandwidth. For larger models, the V100's higher VRAM capacity gives it an advantage.
What is the price difference between V100 and Radeon Pro V520 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, V100 starts at $0.13/hour while Radeon Pro V520 starts at $0.19/hour. This represents a 32% price difference.
Can I use Radeon Pro V520 instead of V100 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 V100, the Radeon Pro V520 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the V100's architecture may be essential.
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