NVIDIA B200 VS AMD Radeon Pro V520
Choosing between **B200** and **Radeon Pro V520** depends on your specific AI workload requirements. The **B200** 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 **$2.25/h** and **$0.19/h** respectively across 21 providers.
Radeon Pro V520
📊 Detailed Specifications Comparison
| Specification | B200 | Radeon Pro V520 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | RDNA 1 | - |
| Process Node | 4nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM | Single-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 192GB | 8GB | +2300% |
| Memory Type | HBM3e | HBM2 | - |
| Memory Bandwidth | 8.0 TB/s | 512 GB/s | +1463% |
| Memory Bus Width | 8192-bit | 2048-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 18,432 | N/A | |
| Tensor Cores (AI) | 576 | N/A | |
| Stream Processors | N/A | 2,304 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 90 TFLOPS | 9.4 TFLOPS | +857% |
| FP16 (Half Precision) | 4,500 TFLOPS | N/A | |
| TF32 (Tensor Float) | 2,250 TFLOPS | N/A | |
| FP64 (Double Precision) | 45 TFLOPS | N/A | |
| INT8 (Integer Precision) | 9,000 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1000W | 225W | +344% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink 5.0 (1.8 TB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA B200
Higher VRAM capacity and memory bandwidth are critical for training large language models. The B200 offers 192GB compared to 8GB.
AI Inference
NVIDIA B200
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
AMD Radeon Pro V520
Based on current cloud pricing, the Radeon Pro V520 starts at a lower hourly rate.
Technical Deep Dive: B200 vs Radeon Pro V520
This head-to-head pits NVIDIA's Blackwell against AMD's RDNA 1. The B200 has a significant **184GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **Radeon Pro V520** is currently about **92% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA B200 is Best For:
- Next-gen LLM training
- Trillion parameter models
- Cost-sensitive projects
AMD Radeon Pro V520 is Best For:
- Cloud gaming
- Virtualization
- AI training
Frequently Asked Questions
Which GPU is better for AI training: B200 or Radeon Pro V520?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The B200 offers 192GB of HBM3e memory with 8.0 TB/s bandwidth, while the Radeon Pro V520 provides 8GB of HBM2 with 512 GB/s bandwidth. For larger models, the B200's higher VRAM capacity gives it an advantage.
What is the price difference between B200 and Radeon Pro V520 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, B200 starts at $2.25/hour while Radeon Pro V520 starts at $0.19/hour. This represents a 1084% price difference.
Can I use Radeon Pro V520 instead of B200 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 B200, the Radeon Pro V520 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the B200's NVLink support (NVLink 5.0 (1.8 TB/s)) may be essential.
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