NVIDIA H100 PCIe VS AMD Instinct MI250
Choosing between **H100 PCIe** and **Instinct MI250** depends on your specific AI workload requirements. While the **Instinct MI250** offers more VRAM for larger models, the **H100 PCIe** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.00/h** and **$1.30/h** respectively across 1 providers.
H100 PCIe
Instinct MI250
📊 Detailed Specifications Comparison
| Specification | H100 PCIe | Instinct MI250 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | CDNA 2 | - |
| Process Node | 4nm | 6nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Dual-slot PCIe | OAM | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 128GB | -38% |
| Memory Type | HBM3 | HBM2e | - |
| Memory Bandwidth | 2.0 TB/s | 3.2 TB/s | -38% |
| Memory Bus Width | 5120-bit | 8192-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | 14,592 | N/A | |
| Tensor Cores (AI) | 456 | N/A | |
| Stream Processors | N/A | 13,312 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 51 TFLOPS | 45.3 TFLOPS | +13% |
| FP16 (Half Precision) | 1,513 TFLOPS | N/A | |
| FP64 (Double Precision) | N/A | 45.3 TFLOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 350W | 500W | -30% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 PCIe
Higher VRAM capacity and memory bandwidth are critical for training large language models. The Instinct MI250 offers 128GB 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
AMD Instinct MI250
Compare live pricing to find the best value for your specific workload.
Technical Deep Dive: H100 PCIe vs Instinct MI250
This head-to-head pits NVIDIA's Hopper against AMD's CDNA 2. The Instinct MI250 has a significant **48GB 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
AMD Instinct MI250 is Best For:
- HPC
- Matrix math workloads
- CUDA native apps
Frequently Asked Questions
Which GPU is better for AI training: H100 PCIe or Instinct MI250?
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 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 H100 PCIe and Instinct MI250 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 Instinct MI250 instead of H100 PCIe 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 H100 PCIe, the Instinct MI250 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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