NVIDIA H100 PCIe VS NVIDIA GH200 Grace Hopper
Choosing between **H100 PCIe** and **GH200** depends on your specific AI workload requirements. The **GH200** 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 **$1.49/h** respectively across 4 providers.
H100 PCIe
📊 Detailed Specifications Comparison
| Specification | H100 PCIe | GH200 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | Hopper + Grace | - |
| Process Node | 4nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Dual-slot PCIe | Superchip | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 96GB | -17% |
| Memory Type | HBM3 | HBM3 | - |
| Memory Bandwidth | 2.0 TB/s | 4.0 TB/s | -50% |
| Memory Bus Width | 5120-bit | 6144-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 14,592 | 16,896 | -14% |
| Tensor Cores (AI) | 456 | 528 | -14% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 51 TFLOPS | 67 TFLOPS | -24% |
| FP16 (Half Precision) | 1,513 TFLOPS | 1,979 TFLOPS | -24% |
| TF32 (Tensor Float) | N/A | 989 TFLOPS | |
| FP64 (Double Precision) | N/A | 34 TFLOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 350W | 900W | -61% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | None | NVLink-C2C (900 GB/s) | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA GH200 Grace Hopper
Higher VRAM capacity and memory bandwidth are critical for training large language models. The GH200 offers 96GB compared to 80GB.
AI Inference
NVIDIA H100 PCIe
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA GH200 Grace Hopper
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: H100 PCIe vs GH200
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper against Hopper + Grace. The GH200 has a significant **16GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA H100 PCIe is Best For:
- AI inference
- Enterprise AI
- Highest-end training
NVIDIA GH200 Grace Hopper is Best For:
- CPU+GPU unified computing
- Large-memory AI workloads
- Standard GPU deployments
Frequently Asked Questions
Which GPU is better for AI training: H100 PCIe or GH200?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H100 PCIe offers 80GB of HBM3 memory with 2.0 TB/s bandwidth, while the GH200 provides 96GB of HBM3 with 4.0 TB/s bandwidth. For larger models, the GH200's higher VRAM capacity gives it an advantage.
What is the price difference between H100 PCIe and GH200 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 GH200 instead of H100 PCIe for my workload?
It depends on your specific requirements. If your model fits within 96GB of VRAM and you don't need the additional throughput of the H100 PCIe, the GH200 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H100 PCIe's architecture may be essential.
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