AMD Instinct MI300A vs GH200

Detailed comparison of specifications, performance, and pricing between AMD Instinct MI300A and GH200

Comparing:
🏆
Overall Winner
AMD Instinct MI300A
Wins 4 of 7 categories
Performance Leader
AMD Instinct MI300A
2.0k TFLOPS (+0%)

Difference Analysis

Metric
AMD Instinct MI300A
Difference
GH200
Tensor TFLOPS
2.0k
=
-
VRAM
128GB
+33%
96GB
Memory Bandwidth
5.3 TB/s
=
-
Hardware Price
-
=
-
Cloud Price/hr
-
=
$2.29

Full Specifications

Specification AMD Instinct MI300A GH200 H100
Brand AMD NVIDIA NVIDIA
Series Data Center - Data Center
Architecture CDNA 3 - Hopper
VRAM 128GB 96GB 80GB
VRAM Type HBM3 - HBM3
Memory Bandwidth 5.3 TB/s - 3.4 TB/s
FP16 TFLOPS 980.0 - 134.0
Tensor TFLOPS 2.0k - 2.0k
TDP 760W - 700W
Form Factor - - SXM
Hardware Price - - $$32k
Cloud Price (min) - $2.29/hr $1.47/hr

Which Should You Choose?

🧠

For AI Training

Large model training needs maximum VRAM and memory bandwidth.

Recommended: AMD Instinct MI300A
128GB VRAM · 5.3 TB/s

For AI Inference

Inference prioritizes throughput and cost efficiency.

Recommended: AMD Instinct MI300A
Best performance per dollar

AMD Instinct MI300A vs GH200 FAQ

It depends on your use case. The AMD Instinct MI300A offers 0% better performance (2.0k vs - TFLOPS). For raw performance, choose AMD Instinct MI300A. For value, consider your budget and workload requirements.

The AMD Instinct MI300A has more VRAM with 128GB compared to 96GB (33% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.

For AI training, the AMD Instinct MI300A is generally better due to its larger VRAM (128GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 96GB, the cheaper option may be more cost-effective.

Price comparison requires both GPUs to have available pricing data. Check individual GPU pages for current market prices.

Upgrading to AMD Instinct MI300A would give you 0% more performance and 33% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.

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