NVIDIA B200 VS NVIDIA V100
Choosing between **B200** and **V100** 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 **$0.13/h** respectively across 37 providers.
📊 Detailed Specifications Comparison
| Specification | B200 | V100 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Volta | - |
| Process Node | 4nm | 12nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM | SXM2 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 192GB | 32GB | +500% |
| Memory Type | HBM3e | HBM2 | - |
| Memory Bandwidth | 8.0 TB/s | 900 GB/s | +789% |
| Memory Bus Width | 8192-bit | 4096-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 18,432 | 5,120 | +260% |
| Tensor Cores (AI) | 576 | 640 | -10% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 90 TFLOPS | 15.7 TFLOPS | +473% |
| FP16 (Half Precision) | 4,500 TFLOPS | 125 TFLOPS | +3500% |
| TF32 (Tensor Float) | 2,250 TFLOPS | N/A | |
| FP64 (Double Precision) | 45 TFLOPS | 7.8 TFLOPS | +477% |
| INT8 (Integer Precision) | 9,000 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1000W | 300W | +233% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 3.0 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 32GB.
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
NVIDIA V100
Based on current cloud pricing, the V100 starts at a lower hourly rate.
Technical Deep Dive: B200 vs V100
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Volta. The B200 has a significant **160GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **V100** is currently about **94% 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
NVIDIA V100 is Best For:
- Deep learning training
- Scientific research
- Latest generation workloads
Frequently Asked Questions
Which GPU is better for AI training: B200 or V100?
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 V100 provides 32GB of HBM2 with 900 GB/s bandwidth. For larger models, the B200's higher VRAM capacity gives it an advantage.
What is the price difference between B200 and V100 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, B200 starts at $2.25/hour while V100 starts at $0.13/hour. This represents a 1631% price difference.
Can I use V100 instead of B200 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 B200, the V100 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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