B200 vs AMD Instinct MI250

Detailed comparison of specifications, performance, and pricing between B200 and AMD Instinct MI250

Comparing:
🏆
Overall Winner
B200
Wins 4 of 7 categories
Performance Leader
B200
4.5k TFLOPS (+522%)
The B200 is 522% faster.

Difference Analysis

Metric
B200
Difference
AMD Instinct MI250
Tensor TFLOPS
4.5k
+522%
724.0
VRAM
192GB
+50%
128GB
Memory Bandwidth
8.0 TB/s
+144%
3.3 TB/s
Hardware Price
$$45k
=
-
Cloud Price/hr
$3.75
=
-

Full Specifications

Specification B200 AMD Instinct MI250 H100 H100 NVL
Brand NVIDIA AMD NVIDIA NVIDIA
Series Data Center Data Center Data Center Data Center
Architecture Blackwell CDNA 2 Hopper Hopper
VRAM 192GB 128GB 80GB 93GB
VRAM Type HBM3e HBM2E HBM3 HBM3
Memory Bandwidth 8.0 TB/s 3.3 TB/s 3.4 TB/s 3.9 TB/s
FP16 TFLOPS - 362.0 134.0 134.0
Tensor TFLOPS 4.5k 724.0 2.0k 2.0k
TDP 1000W 500W 700W 400W
Form Factor SXM - SXM NVL
Hardware Price $$45k - $$32k $$35k
Cloud Price (min) $3.75/hr - $1.47/hr $1.33/hr

Which Should You Choose?

🧠

For AI Training

Large model training needs maximum VRAM and memory bandwidth.

Recommended: B200
192GB VRAM · 8.0 TB/s

For AI Inference

Inference prioritizes throughput and cost efficiency.

Recommended: B200
Best performance per dollar

B200 vs AMD Instinct MI250 FAQ

It depends on your use case. The B200 offers 522% better performance (4.5k vs 724.0 TFLOPS). For raw performance, choose B200. For value, consider your budget and workload requirements.

The B200 has more VRAM with 192GB compared to 128GB (50% more). More VRAM is crucial for training large models and running inference on bigger batch sizes.

For AI training, the B200 is generally better due to its larger VRAM (192GB). Large language models and deep learning workloads benefit significantly from more memory. However, if your models fit in 128GB, 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 B200 would give you 522% more performance and 50% more VRAM. Consider if your workloads are bottlenecked by current GPU capabilities.

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