NVIDIA GH200 Grace Hopper VS AMD Instinct MI300X
Choosing between **GH200** and **Instinct MI300X** 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 **$1.49/h** and **$0.95/h** respectively across 10 providers.
Instinct MI300X
📊 Detailed Specifications Comparison
| Specification | GH200 | Instinct MI300X | Difference |
|---|---|---|---|
| Architecture & Design | |||
| Architecture | Hopper + Grace | CDNA 3 | - |
| Process Node | 4nm | 5nm + 6nm | - |
| Target Market | datacenter | datacenter | - |
| Form Factor | Superchip | OAM | - |
| Memory & Bandwidth | |||
| VRAM Capacity | 96GB | 192GB | -50% |
| Memory Type | HBM3 | HBM3 | - |
| Memory Bandwidth | 4.0 TB/s | 5.3 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 | 19,456 | |
| AI & Compute Performance (TFLOPS) | |||
| FP32 (Single Precision) | 67 TFLOPS | 163.4 TFLOPS | -59% |
| FP16 (Half Precision) | 1,979 TFLOPS | 1,307.4 TFLOPS | +51% |
| TF32 (Tensor Float) | 989 TFLOPS | N/A | |
| FP64 (Double Precision) | 34 TFLOPS | 81.7 TFLOPS | -58% |
| INT8 (Integer Precision) | N/A | 2,614.9 TOPS | |
| Power & Efficiency | |||
| TDP (Thermal Design Power) | 900W | 750W | +20% |
| PCIe Interface | PCIe 5.0 x16 | PCIe 5.0 x16 | - |
| Multi-GPU Interconnect | NVLink-C2C (900 GB/s) | None | - |
🎯 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 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 MI300X
Based on current cloud pricing, the Instinct MI300X starts at a lower hourly rate.
Technical Deep Dive: GH200 vs Instinct MI300X
This head-to-head pits NVIDIA's Hopper + Grace against AMD's CDNA 3. The Instinct MI300X has a significant **96GB VRAM advantage**, which is crucial for training massive datasets or large language models. From a cost perspective, the **Instinct MI300X** is currently about **36% 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 MI300X is Best For:
- LLM inference at scale
- Large VRAM capacity
- CUDA-only software
Frequently Asked Questions
Which GPU is better for AI training: GH200 or Instinct MI300X?
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 MI300X provides 192GB of HBM3 with 5.3 TB/s bandwidth. For larger models, the Instinct MI300X's higher VRAM capacity gives it an advantage.
What is the price difference between GH200 and Instinct MI300X in the cloud?
Cloud GPU rental prices vary by provider and region. Based on our data, GH200 starts at $1.49/hour while Instinct MI300X starts at $0.95/hour. This represents a 57% price difference.
Can I use Instinct MI300X instead of GH200 for my workload?
It depends on your specific requirements. If your model fits within 192GB of VRAM and you don't need the additional throughput of the GH200, the Instinct MI300X 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.
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