NVIDIA GB200 NVL72 VS NVIDIA H100 PCIe
Choosing between **GB200** and **H100 PCIe** 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 **$0.00/h** respectively across 3 providers.
H100 PCIe
📊 Detailed Specifications Comparison
| Specification | GB200 | H100 PCIe | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Blackwell | Hopper | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Rack-scale | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 384GB | 80GB | +380% |
| Memory Type | HBM3e | HBM3 | - |
| Memory Bandwidth | 16.0 TB/s | 2.0 TB/s | +700% |
| Memory Bus Width | 8192-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 36,864 | 14,592 | +153% |
| Tensor Cores (AI) | N/A | 456 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 180 TFLOPS | 51 TFLOPS | +253% |
| FP16 (Half Precision) | 9,000 TFLOPS | 1,513 TFLOPS | +495% |
| INT8 (Integer Precision) | 18,000 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 1200W | 350W | +243% |
| 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 80GB.
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: GB200 vs H100 PCIe
This is a generational comparison within the NVIDIA ecosystem, pitting Blackwell against Hopper. The GB200 has a significant **304GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA GB200 NVL72 is Best For:
- Massive LLM training
- Trillion-parameter models
- Single-node tasks
NVIDIA H100 PCIe is Best For:
- AI inference
- Enterprise AI
- Highest-end training
Frequently Asked Questions
Which GPU is better for AI training: GB200 or H100 PCIe?
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 H100 PCIe provides 80GB of HBM3 with 2.0 TB/s bandwidth. For larger models, the GB200's higher VRAM capacity gives it an advantage.
What is the price difference between GB200 and H100 PCIe 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 H100 PCIe instead of GB200 for my workload?
It depends on your specific requirements. If your model fits within 80GB of VRAM and you don't need the additional throughput of the GB200, the H100 PCIe 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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