NVIDIA GH200 Grace Hopper VS AMD Instinct MI250
Choosing between **GH200** and **Instinct MI250** depends on your specific AI workload requirements. While the **Instinct MI250** offers more VRAM for larger models, the **GH200** remains competitive in other areas. Currently, you can rent these GPUs starting from **$1.49/h** and **$1.30/h** respectively across 5 providers.
Instinct MI250
📊 Detailed Specifications Comparison
| Specification | GH200 | Instinct MI250 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper + Grace | CDNA 2 | - |
| Process Node | 4nm | 6nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Superchip | OAM | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 96GB | 128GB | -25% |
| Memory Type | HBM3 | HBM2e | - |
| Memory Bandwidth | 4.0 TB/s | 3.2 TB/s | +25% |
| Memory Bus Width | 6144-bit | 8192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 16,896 | N/A | |
| Tensor Cores (AI) | 528 | N/A | |
| Stream Processors | N/A | 13,312 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 45.3 TFLOPS | +48% |
| FP16 (Half Precision) | 1,979 TFLOPS | N/A | |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | 45.3 TFLOPS | -25% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 900W | 500W | +80% |
| 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 Instinct MI250 offers 128GB compared to 96GB.
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
AMD Instinct MI250
Based on current cloud pricing, the Instinct MI250 starts at a lower hourly rate.
Technical Deep Dive: GH200 vs Instinct MI250
This head-to-head pits NVIDIA's Hopper + Grace against AMD's CDNA 2. The Instinct MI250 has a significant **32GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **Instinct MI250** is currently about **13% 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
AMD Instinct MI250 is Best For:
- HPC
- Matrix math workloads
- CUDA native apps
Frequently Asked Questions
Which GPU is better for AI training: GH200 or Instinct MI250?
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 Instinct MI250 provides 128GB of HBM2e with 3.2 TB/s bandwidth. For larger models, the Instinct MI250's higher VRAM capacity gives it an advantage.
What is the price difference between GH200 and Instinct MI250 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GH200 starts at $1.49/hour while Instinct MI250 starts at $1.30/hour. This represents a 15% price difference.
Can I use Instinct MI250 instead of GH200 for my workload?
It depends on your specific requirements. If your model fits within 128GB of VRAM and you don't need the additional throughput of the GH200, the Instinct MI250 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.
Ready to rent a GPU?
Compare live pricing across 50+ cloud providers and find the best deal.