H100 vs AMD Instinct MI210

Detailed comparison of specifications, performance, and pricing between H100 and AMD Instinct MI210

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Overall Winner
H100
Wins 4 of 7 categories
Performance Leader
H100
2.0k TFLOPS (+447%)
The H100 is 447% faster.

Difference Analysis

Metric
H100
Difference
AMD Instinct MI210
Tensor TFLOPS
2.0k
+447%
362.0
VRAM
80GB
+25%
64GB
Memory Bandwidth
3.4 TB/s
+105%
1.6 TB/s
Hardware Price
$$32k
=
-
Cloud Price/hr
$1.47
=
-

Full Specifications

Specification H100 AMD Instinct MI210 H100 PCIe H100 NVL
Brand NVIDIA AMD NVIDIA NVIDIA
Series Data Center Data Center Data Center Data Center
Architecture Hopper CDNA 2 Hopper Hopper
VRAM 80GB 64GB 80GB 93GB
VRAM Type HBM3 HBM2E HBM2e HBM3
Memory Bandwidth 3.4 TB/s 1.6 TB/s 2.0 TB/s 3.9 TB/s
FP16 TFLOPS 134.0 181.0 102.0 134.0
Tensor TFLOPS 2.0k 362.0 1.5k 2.0k
TDP 700W 300W 350W 400W
Form Factor SXM - PCIe NVL
Hardware Price $$32k - $$28k $$35k
Cloud Price (min) $1.47/hr - $2.89/hr $1.33/hr

Which Should You Choose?

🧠

For AI Training

Large model training needs maximum VRAM and memory bandwidth.

Recommended: H100
80GB VRAM · 3.4 TB/s

For AI Inference

Inference prioritizes throughput and cost efficiency.

Recommended: H100
Best performance per dollar

H100 vs AMD Instinct MI210 FAQ

It depends on your use case. The H100 offers 447% better performance (2.0k vs 362.0 TFLOPS). For raw performance, choose H100. For value, consider your budget and workload requirements.

The H100 has more VRAM with 80GB compared to 64GB (25% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.

For AI training, the H100 is generally better due to its larger VRAM (80GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 64GB, 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 H100 would give you 447% more performance and 25% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.

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