NVIDIA GH200 Grace Hopper VS NVIDIA Tesla P100
Choosing between **GH200** and **P100** 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 **$0.08/h** respectively across 10 providers.
📊 Detailed Specifications Comparison
| Specification | GH200 | P100 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper + Grace | Pascal | - |
| Process Node | 4nm | 16nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Superchip | Dual-slot PCIe | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 96GB | 16GB | +500% |
| Memory Type | HBM3 | HBM2 | - |
| Memory Bandwidth | 4.0 TB/s | 732 GB/s | +446% |
| Memory Bus Width | 6144-bit | 4096-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | 3,584 | +371% |
| Tensor Cores (AI) | 528 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 9.3 TFLOPS | +620% |
| FP16 (Half Precision) | 1,979 TFLOPS | N/A | |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 900W | 300W | +200% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 3.0 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 16GB.
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 Tesla P100
Based on current cloud pricing, the P100 starts at a lower hourly rate.
Technical Deep Dive: GH200 vs P100
This is a generational comparison within the NVIDIA ecosystem, pitting Hopper + Grace against Pascal. The GH200 has a significant **80GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **P100** is currently about **95% 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 Tesla P100 is Best For:
- Legacy AI workloads
- Precision-heavy training
Frequently Asked Questions
Which GPU is better for AI training: GH200 or P100?
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 P100 provides 16GB of HBM2 with 732 GB/s bandwidth. For larger models, the GH200's higher VRAM capacity gives it an advantage.
What is the price difference between GH200 and P100 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GH200 starts at $1.49/hour while P100 starts at $0.08/hour. This represents a 1763% price difference.
Can I use P100 instead of GH200 for my workload?
It depends on your specific requirements. If your model fits within 16GB of VRAM and you don't need the additional throughput of the GH200, the P100 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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