NVIDIA B100 VS NVIDIA GB200 NVL72
Choosing between **B100** and **GB200** 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 **$0.00/h** and **$10.50/h** respectively across 3 providers.
📊 Detailed Specifications Comparison
| Specification | B100 | GB200 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Blackwell | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM | Rack-scale | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 192GB | 384GB | -50% |
| Memory Type | HBM3e | HBM3e | - |
| Memory Bandwidth | 8.0 TB/s | 16.0 TB/s | -50% |
| Memory Bus Width | 8192-bit | 8192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 14,336 | 36,864 | -61% |
| Tensor Cores (AI) | 448 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 70 TFLOPS | 180 TFLOPS | -61% |
| FP16 (Half Precision) | 3,500 TFLOPS | 9,000 TFLOPS | -61% |
| TF32 (Tensor Float) | 1,750 TFLOPS | N/A | |
| FP64 (Double Precision) | 35 TFLOPS | N/A | |
| INT8 (Integer Precision) | 7,000 TOPS | 18,000 TOPS | -61% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 1200W | -42% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 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 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 GB200 NVL72
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: B100 vs GB200
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.
NVIDIA B100 is Best For:
- Large-scale AI training
- Budget deployments
NVIDIA GB200 NVL72 is Best For:
- Massive LLM training
- Trillion-parameter models
- Single-node tasks
Frequently Asked Questions
Which GPU is better for AI training: B100 or GB200?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The B100 offers 192GB of HBM3e memory with 8.0 TB/s bandwidth, while the GB200 provides 384GB of HBM3e with 16.0 TB/s bandwidth. For larger models, the GB200's higher VRAM capacity gives it an advantage.
What is the price difference between B100 and GB200 in the cloud?
Cloud GPU rental prices vary by provider and region. Check our price tracker for the latest rates from 50+ cloud providers.
Can I use GB200 instead of B100 for my workload?
It depends on your specific requirements. If your model fits within 384GB of VRAM and you don't need the additional throughput of the B100, the GB200 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the B100's architecture may be essential.
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