NVIDIA GH200 Grace Hopper VS NVIDIA A100 80GB
Choosing between **GH200** and **A100 80GB** 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 **$1.49/h** and **$0.40/h** respectively across 45 providers.
A100 80GB
📊 Detailed Specifications Comparison
| Specification | GH200 | A100 80GB | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper + Grace | Ampere | - |
| Process Node | 4nm | 7nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Superchip | SXM4 / PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 96GB | 80GB | +20% |
| Memory Type | HBM3 | HBM2e | - |
| Memory Bandwidth | 4.0 TB/s | 2.0 TB/s | +96% |
| Memory Bus Width | 6144-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | 6,912 | +144% |
| Tensor Cores (AI) | 528 | 432 | +22% |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 19.5 TFLOPS | +244% |
| FP16 (Half Precision) | 1,979 TFLOPS | 312 TFLOPS | +534% |
| TF32 (Tensor Float) | 989 TFLOPS | 156 TFLOPS | +534% |
| FP64 (Double Precision) | 34 TFLOPS | 9.7 TFLOPS | +251% |
| INT8 (Integer Precision) | N/A | 624 TOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 900W | 400W | +125% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink-C2C (900 GB/s) | NVLink 3.0 (600 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 GH200 Grace Hopper
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA A100 80GB
Based on current cloud pricing, the A100 80GB starts at a lower hourly rate.
Technical Deep Dive: GH200 vs A100 80GB
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper + Grace against Ampere. The GH200 has a significant **16GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **A100 80GB** is currently about **73% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA GH200 Grace Hopper is Best For:
- CPU+GPU unified computing
- Large-memory AI workloads
- Standard GPU deployments
NVIDIA A100 80GB is Best For:
- AI model training
- Scientific computing
- Newest FP8 precision workloads
Frequently Asked Questions
Which GPU is better for AI training: GH200 or A100 80GB?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The GH200 offers 96GB of HBM3 memory with 4.0 TB/s bandwidth, while the A100 80GB provides 80GB of HBM2e with 2.0 TB/s bandwidth. For larger models, the GH200's higher VRAM capacity gives it an advantage.
What is the price difference between GH200 and A100 80GB in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GH200 starts at $1.49/hour while A100 80GB starts at $0.40/hour. This represents a 272% price difference.
Can I use A100 80GB instead of GH200 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 GH200, the A100 80GB can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the GH200's NVLink support (NVLink-C2C (900 GB/s)) may be essential.
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