The launch of NVIDIA’s B200 GPU has sparked intense interest across the AI community. With its groundbreaking Blackwell architecture, 208 billion transistors, and 20 PFLOPS of FP4 compute power, the B200 promises revolutionary performance for large-scale AI workloads. Yet many users wonder: When does the B200 truly outperform predecessors like H200, and is it worth the investment? To answer this, we rigorously tested B200 against H200 across compute, communication, and batch-training scenarios.
NVIDIA BLACKWELL B200 MACHINE SPECT
GPU: 8 x NVIDIA H200-SXM5-180GB
GPU Memory: 1,440GB total GPU memory
CPU: 2 x Intel Xeon Platinum 8580
Memory: 32 x DDR5 5600MHz 64GB
System disk: 2 x 960GB NVMe SSD
Data disk: 8 x 7.68TB NVMe SSD
Compute Network: 8 x 400Gb/s InfiniBand
Storage Network: 200Gb/s
In-Band Network: 25Gb/s
Why NVIDIA B200?
According to
Nvidia website, the NVIDIA Blackwell architecture—powering the HGX B200 platform—delivers a significant generational leap over the Hopper generation, achieving:
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Up to 2.25x the throughput of NVIDIA HGX H200
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4x Faster training performance for LLMs
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15x Higher inference throughput for real-time applications
With 180GB of blazing-fast HBM3e memory per GPU, the NVIDIA Blackwell architecture is tailor-made for modern AI workloads.
We have performed rigorous tests on B200 against H200 in the following scenarios.
Compute Performance: GEMM Operator Benchmark
GEMM (General Matrix Multiply) is a fundamental operation in deep learning, and its performance is a key indicator of a GPU's computational power. We evaluated GEMM operations across batch sizes [8, 16, 32, 64]and matrix dimensions [1024, 2048, 4096, 8192], simulating dense AI computations like transformer layers.
Our test results reveal B200 has decisive advantage:
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Configuration
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Avg. Throughput
|
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H200
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652.40 TFLOPS
|
|
B200
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1420.40 TFLOPS
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Interpretation:
As the results show, the B200 demonstrated a substantial performance advantage, averaging nearly 2x throughput of the H200 across all test cases. This indicates that for compute-heavy tasks(e.g., LLM fine-tuning or high-resolution vision models)where low-precision ops dominate, B200 provides a significant boost.
Scaling Efficiency: All-Reduce Communication Benchmark
For large-scale distributed training, communication between GPUs is just as important as individual card performance. We benchmarked the All-Reduce communication primitive using both PyTorch Distributed and NCCL tests.
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Test Configuration
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Performance |
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Method
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GPU
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Count
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Average Throughput
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Peak Throughput
|
|
PyTorch Distributed
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H200
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8
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245.359 GB/s
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402.41 GB/s
|
|
16
|
187.076 GB/s
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370.36 GB/s
|
|
B200
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8
|
293.638 GB/s
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572.44 GB/s
|
|
16
|
190.993 GB/s
|
438.85 GB/s
|
|
NCCL Test
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H200
|
8
|
137.711 GB/s
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437.2 GB/s
|
|
16
|
|
666.0 GB/s
|
|
B200
|
8
|
241.32 GB/s
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399.5 GB/s
|
|
16
|
165.915 GB/s
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522.8 GB/s
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Key Observations:
The B200 showed a noticeable improvement in communication throughput, particularly in the PyTorch Distributed tests, where it outperformed the H200 on both 8-card and 16-card setups. This enhanced communication performance is crucial for large-scale model training and inference.
Colossal-AI Benchmark
To see how these performance gains translate to real-world applications, we conducted a large language model training benchmark using Colossal-AI on Llama-like models. The tests were run on both 8-card and 16-card configurations for 7B and 70B models, respectively. For further details on Colossal-AI—an AI system designed to accelerate and lower the cost of model training and inference—please refer to
https://github.com/hpcaitech/ColossalAI.
|
GPU
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GPUs
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Model size
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Parallelism
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Batch Size per DP
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Seqlen
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Throughput
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TFLOPS/GPU
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Peak Mem (MiB)
|
|
H200
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8
|
7B
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zero2(dp8)
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36
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4096
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17.13 samp/s
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534.18
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119040.02
|
|
16
|
70B
|
zero2
|
48
|
4096
|
3.27 samp/s
|
469.1
|
150032.23
|
|
B200
|
8
|
7B
|
zero1(dp2) + tp2 + pp4
|
128
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4096
|
25.83 samp/s
|
805.69
|
100119.77
|
|
16
|
70B
|
zero1(dp2) + tp2 + pp4
|
128
|
4096
|
5.66 samp/s
|
811.79
|
100072.02
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The results from the Colossal-AI benchmark provide the most practical insight. For the 7B model on 8 cards, the B200 achieved a 50% higher throughput and a significant increase in TFLOPS per GPU. For the 70B model on 16 cards, the B200 again demonstrated a clear advantage, with over 70% higher throughput and TFLOPS per GPU. These numbers show that the B200's performance gains translate directly to faster training times for large-scale models.
Conclusion
Based on our benchmarks, the Nvidia B200 GPU consistently outperforms the H200 across key metrics for AI workloads.
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For pure computational tasks, B200 offers nearly double the throughput, which is ideal for models that are heavily compute-bound.
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For distributed training, B200's improved communication performance contributes to better overall scaling, as seen in the PyTorch Distributed and Colossal-AI benchmarks.
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In real-world LLM training scenarios, B200 provides a substantial advantage, leading to significantly higher sample throughput and TFLOPS per GPU. This translates directly to faster training and inference times, making the B200 an excellent choice for organizations that need to accelerate their AI development cycle.
In summary, if your workload involves large-scale, compute-intensive AI models and you're looking for the fastest possible training and inference times, the B200 is the clear choice for a performance upgrade.
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(Keywords: NVIDIA Blackwell GPU server, HPC-AI.COM, B200 GPU, multi-node GPU cluster, AI cloud for LLM training, cheapest H200 rental, 8-card GPU server pricing)
Data Sources: All benchmarks conducted on HPC-AI.COM’s DGX B200/H200 clusters with NVIDIA's NGC image nvcr.io/nvidia/pytorch:25.04-py3 (PyTorch 2.7.0 + CUDA 12.8), other required libraries were locally built.