AMD Instinct MI300X VS NVIDIA H100 SXM
Choosing between **Instinct MI300X** and **H100 SXM** depends on your specific AI workload requirements. The **Instinct MI300X** 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 **$0.95/h** and **$0.73/h** respectively across 52 providers.
Instinct MI300X
📊 Detailed Specifications Comparison
| Specification | Instinct MI300X | H100 SXM | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | CDNA 3 | Hopper | - |
| Process Node | 5nm + 6nm | 4nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | OAM | SXM5 | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 192GB | 80GB | +140% |
| Memory Type | HBM3 | HBM3 | - |
| Memory Bandwidth | 5.3 TB/s | 3.35 TB/s | +58% |
| Memory Bus Width | 8192-bit | 5120-bit | - |
| Compute Infrastructure | |||
| CUDA Cores | N/A | 16,896 | |
| Tensor Cores (AI) | N/A | 528 | |
| Stream Processors | 19,456 | N/A | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 163.4 TFLOPS | 67 TFLOPS | +144% |
| FP16 (Half Precision) | 1,307.4 TFLOPS | 1,979 TFLOPS | -34% |
| TF32 (Tensor Float) | N/A | 989 TFLOPS | |
| FP64 (Double Precision) | 81.7 TFLOPS | 34 TFLOPS | +140% |
| INT8 (Integer Precision) | 2,614.9 TOPS | 3,958 TOPS | -34% |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 750W | 700W | +7% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | None | NVLink 4.0 (900 GB/s) | - |
🎯 Use Case Recommendations
LLM & Large Model Training
AMD Instinct MI300X
Higher VRAM capacity and memory bandwidth are critical for training large language models. The Instinct MI300X offers 192GB 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: Instinct MI300X vs H100 SXM
This head-to-head pits AMD's CDNA 3 against NVIDIA's Hopper. The Instinct MI300X has a significant **112GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **H100 SXM** is currently about **23% cheaper** per hour, offering better value for budget-conscious projects.
AMD Instinct MI300X is Best For:
- LLM inference at scale
- Large VRAM capacity
- CUDA-only software
NVIDIA H100 SXM is Best For:
- LLM training
- Foundation model pre-training
- Small-scale inference
Frequently Asked Questions
Which GPU is better for AI training: Instinct MI300X or H100 SXM?
For AI training, the key factors are VRAM size, memory bandwidth, and tensor core performance. The Instinct MI300X offers 192GB of HBM3 memory with 5.3 TB/s bandwidth, while the H100 SXM provides 80GB of HBM3 with 3.35 TB/s bandwidth. For larger models, the Instinct MI300X's higher VRAM capacity gives it an advantage.
What is the price difference between Instinct MI300X and H100 SXM in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, Instinct MI300X starts at $0.95/hour while H100 SXM starts at $0.73/hour. This represents a 30% price difference.
Can I use H100 SXM instead of Instinct MI300X for my workload?
It depends on your specific requirements. If your model fits within 80GB of VRAM and you don't need the additional throughput of the Instinct MI300X, the H100 SXM can be a cost-effective alternative. However, for workloads requiring maximum memory capacity or multi-GPU scaling, the Instinct MI300X's architecture may be essential.
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