NVIDIA H100 SXM VS AMD Instinct MI325X
Choosing between **H100 SXM** and **Instinct MI325X** depends on your specific AI workload requirements. The **Instinct MI325X** 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 **$0.73/h** and **$1.69/h** respectively across 49 providers.
Instinct MI325X
📊 Detailed Specifications Comparison
| Specification | H100 SXM | Instinct MI325X | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper | CDNA 3 | - |
| Process Node | 4nm | 5nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | SXM5 | OAM | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 80GB | 256GB | -69% |
| Memory Type | HBM3 | HBM3e | - |
| Memory Bandwidth | 3.35 TB/s | 6.0 TB/s | -44% |
| 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 | 19,456 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 163 TFLOPS | -59% |
| FP16 (Half Precision) | 1,979 TFLOPS | 2,600 TFLOPS | -24% |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | N/A | |
| INT8 (Integer Precision) | 3,958 TOPS | N/A | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 700W | 750W | -7% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | NVLink 4.0 (900 GB/s) | None | - |
🎯 Use Case Recommendations
LLM & Large Model Training
AMD Instinct MI325X
Higher VRAM capacity and memory bandwidth are critical for training large language models. The Instinct MI325X offers 256GB compared to 80GB.
AI Inference
AMD Instinct MI325X
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 MI325X
This head-to-head pits NVIDIA's Hopper against AMD's CDNA 3. The Instinct MI325X has a significant **176GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **H100 SXM** is currently about **57% 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 MI325X is Best For:
- AI training
- Large model inference
- CUDA-only software
Frequently Asked Questions
Which GPU is better for AI training: H100 SXM or Instinct MI325X?
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 MI325X provides 256GB of HBM3e with 6.0 TB/s bandwidth. For larger models, the Instinct MI325X's higher VRAM capacity gives it an advantage.
What is the price difference between H100 SXM and Instinct MI325X 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 MI325X starts at $1.69/hour. This represents a 57% price difference.
Can I use Instinct MI325X instead of H100 SXM for my workload?
It depends on your specific requirements. If your model fits within 256GB of VRAM and you don't need the additional throughput of the H100 SXM, the Instinct MI325X 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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