NVIDIA H100 SXM VS AMD Instinct MI250
Choosing between **H100 SXM** and **Instinct MI250** depends on your specific AI workload requirements. While the **Instinct MI250** offers more VRAM for larger models, the **H100 SXM** remains competitive in other areas. Currently, you can rent these GPUs starting from **$0.73/h** and **$1.30/h** respectively across 47 providers.
Instinct MI250
📊 Detailed Specifications Comparison
| Specification | H100 SXM | Instinct MI250 | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | CDNA 2 | - |
| Process Node | 4nm | 6nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | OAM | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 128GB | -38% |
| Memory Type | HBM3 | HBM2e | - |
| Memory Bandwidth | 3.35 TB/s | 3.2 TB/s | +5% |
| Memory Bus Width | 5120-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% |
| INT8 (Integer Precision) | 3,958 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 500W | +40% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 4.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
NVIDIA H100 SXM
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 SXM
For inference workloads, performance per watt matters most. Consider the balance between FP16/INT8 throughput and power consumption.
Budget-Conscious Choice
NVIDIA H100 SXM
Based on current cloud pricing, the H100 SXM starts at a lower hourly rate.
Technical Deep Dive: H100 SXM 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. From a cost perspective, the **H100 SXM** is currently about **44% cheaper** per hour, offering better value for budget-conscious projects.
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
AMD Instinct MI250 is Best For:
- HPC
- Matrix math workloads
- CUDA native apps
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or Instinct MI250?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The H100 SXM offers 80GB of HBM3 memory with 3.35 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 SXM and Instinct MI250 in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, H100 SXM starts at $0.73/hour while Instinct MI250 starts at $1.30/hour. This represents a 44% price difference.
Can I use Instinct MI250 instead of H100 SXM 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 SXM, the Instinct MI250 can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the H100 SXM's NVLink support (NVLink 4.0 (900 GB/s)) may be essential.
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