NVIDIA GB200 NVL72 VS NVIDIA V100
Choosing between **GB200** and **V100** depends on your specific AI workload requirements. The **GB200** 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 **$10.50/h** and **$0.13/h** respectively across 20 providers.
📊 Detailed Specifications Comparison
| Specification | GB200 | V100 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Volta | - |
| Process Node | 4nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Rack-scale | SXM2 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 384GB | 32GB | +1100% |
| Memory Type | HBM3e | HBM2 | - |
| Memory Bandwidth | 16.0 TB/s | 900 GB/s | +1678% |
| Memory Bus Width | 8192-bit | 4096-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 36,864 | 5,120 | +620% |
| Tensor Cores (AI) | N/A | 640 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 180 TFLOPS | 15.7 TFLOPS | +1046% |
| FP16 (Half Precision) | 9,000 TFLOPS | 125 TFLOPS | +7100% |
| FP64 (Double Precision) | N/A | 7.8 TFLOPS | |
| INT8 (Integer Precision) | 18,000 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1200W | 300W | +300% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 3.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA GB200 NVL72
Higher VRAM capacity and memory bandwidth are critical for training large language models. The GB200 offers 384GB compared to 32GB.
AI Inference
NVIDIA GB200 NVL72
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: GB200 vs V100
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Volta. The GB200 has a significant **352GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **V100** is currently about **99% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA GB200 NVL72 is Best For:
- Massive LLM training
- Trillion-parameter models
- Single-node tasks
NVIDIA V100 is Best For:
- Deep learning training
- Scientific research
- Latest generation workloads
Frequently Asked Questions
Which GPU is better for AI training: GB200 or V100?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The GB200 offers 384GB of HBM3e memory with 16.0 TB/s bandwidth, while the V100 provides 32GB of HBM2 with 900 GB/s bandwidth. For larger models, the GB200's higher VRAM capacity gives it an advantage.
What is the price difference between GB200 and V100 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GB200 starts at $10.50/hour while V100 starts at $0.13/hour. This represents a 7977% price difference.
Can I use V100 instead of GB200 for my workload?
It depends on your specific requirements. If your model fits within 32GB of VRAM and you don't need the additional throughput of the GB200, the V100 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the GB200's architecture may be essential.
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