NVIDIA GH200 Grace Hopper VS NVIDIA A100 40GB
Choosing between **GH200** and **A100 40GB** 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 **$0.00/h** respectively across 4 providers.
A100 40GB
📊 Detailed Specifications Comparison
| Specification | GH200 | A100 40GB | 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 | 40GB | +140% |
| Memory Type | HBM3 | HBM2 | - |
| Memory Bandwidth | 4.0 TB/s | 1.5 TB/s | +157% |
| 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 | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 900W | 250W | +260% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink-C2C (900 GB/s) | None | - |
🎯 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 40GB.
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 GH200 Grace Hopper
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: GH200 vs A100 40GB
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper + Grace against Ampere. The GH200 has a significant **56GB VRAM advantage**, which is crucial for training massive datasets or large language models.
NVIDIA GH200 Grace Hopper is Best For:
- CPU+GPU unified computing
- Large-memory AI workloads
- Standard GPU deployments
NVIDIA A100 40GB is Best For:
- Mainstream AI training
- Scientific computing
- Memory-intensive LLM training
Frequently Asked Questions
Which GPU is better for AI training: GH200 or A100 40GB?
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 40GB provides 40GB of HBM2 with 1.5 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 40GB 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 A100 40GB instead of GH200 for my workload?
It depends on your specific requirements. If your model fits within 40GB of VRAM and you don't need the additional throughput of the GH200, the A100 40GB 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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