NVIDIA GB200 NVL72 VS NVIDIA B200
Choosing between **GB200** and **B200** 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 **$2.25/h** respectively across 23 providers.
📊 Detailed Specifications Comparison
| Specification | GB200 | B200 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Blackwell | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Rack-scale | SXM | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 384GB | 192GB | +100% |
| Memory Type | HBM3e | HBM3e | - |
| Memory Bandwidth | 16.0 TB/s | 8.0 TB/s | +100% |
| Memory Bus Width | 8192-bit | 8192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 36,864 | 18,432 | +100% |
| Tensor Cores (AI) | N/A | 576 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 180 TFLOPS | 90 TFLOPS | +100% |
| FP16 (Half Precision) | 9,000 TFLOPS | 4,500 TFLOPS | +100% |
| TF32 (Tensor Float) | N/A | 2,250 TFLOPS | |
| FP64 (Double Precision) | N/A | 45 TFLOPS | |
| INT8 (Integer Precision) | 18,000 TOPS | 9,000 TOPS | +100% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1200W | 1000W | +20% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | None | NVLink 5.0 (1.8 TB/s) | - |
🎯 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 192GB.
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 B200
Based on current cloud pricing, the B200 starts at a lower hourly rate.
Technical Deep Dive: GB200 vs B200
Both GPUs utilize the NVIDIA Blackwell architecture. The primary difference lies in their memory capacity and compute core counts. The GB200 has a significant **192GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **B200** is currently about **79% 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 B200 is Best For:
- Next-gen LLM training
- Trillion parameter models
- Cost-sensitive projects
Frequently Asked Questions
Which GPU is better for AI training: GB200 or B200?
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 B200 provides 192GB of HBM3e with 8.0 TB/s bandwidth. For larger models, the GB200's higher VRAM capacity gives it an advantage.
What is the price difference between GB200 and B200 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GB200 starts at $10.50/hour while B200 starts at $2.25/hour. This represents a 367% price difference.
Can I use B200 instead of GB200 for my workload?
It depends on your specific requirements. If your model fits within 192GB of VRAM and you don't need the additional throughput of the GB200, the B200 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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