NVIDIA H200 vs. B200: A Comprehensive AI GPU Comparison

NVIDIA’s H200 and B200 GPUs represent the peak of data center acceleration, driving the latest breakthroughs in artificial intelligence and high-performance computing. Both architectures are designed to handle the increasing computational and memory demands of modern AI workloads, from large language models to advanced scientific simulations.

This article is a comprehensive H200 vs. B200 guide, which will help you understand their architectural differences, performance capabilities, and ideal applications so that you can make informed investment decisions that align with your AI and data infrastructure goals.

Hopper vs. Blackwell: The Core Architectural Difference

At the heart of B200 vs. H200 is an architectural difference: Blackwell vs. Hopper.

1. NVIDIA H200: The Peak of the Hopper Architecture

The H200 is the ultimate evolution of the Hopper architecture, focusing on eliminating a critical bottleneck for large models: memory capacity and bandwidth. It represents the most powerful drop-in upgrade for existing Hopper-based infrastructure.

Massive and Fast Memory:

It is the first GPU to feature 141GB of ultra-fast HBM3e memory. This is a 76% increase in capacity and a 43% boost in bandwidth (8 TB/s) compared to the H100.
Benefit: This immense memory capacity is crucial for running extremely large or “long-context” LLMs (e.g., Llama2 70B and beyond) entirely on the GPU, significantly accelerating inference speed by up to 2X over its predecessor by eliminating data movement bottlenecks.

Proven AI Foundation:

It retains the powerful Fourth-Generation Tensor Cores and the Transformer Engine, ensuring state-of-the-art performance for mixed-precision (including FP8, FP16, and BF16) AI training and fine-tuning.

Infrastructure Compatibility:

Crucially, the H200 maintains the 700W TDP of the H100, making it an ideal, power-efficient upgrade for organizations looking to boost performance in existing data center footprints without major infrastructure overhauls.

2. NVIDIA B200: The Generational Blackwell Leap

The B200, the flagship of the Blackwell architecture, is not just an iterative improvement. The Nvidia B200 specs show that it is a fundamental re-engineering of the AI accelerator, designed to solve the challenges of trillion-parameter models and hyperscale inference.

Unprecedented Computational Scale:

The B200 boasts a massive increase in raw processing power, achieving up to 20 PetaFLOPS of FP4 AI performance from a single GPU.
Benefit: This performance boost translates to an estimated 3X faster training and a phenomenal 15X faster inference for the largest, most complex models compared to the previous generation.

Next-Gen Memory and Interconnect:

Features 192GB of HBM3e memory with a staggering 8.0 TB/s of bandwidth, ensuring data can be fed to the computational cores at unprecedented speeds.
The Fifth-Generation NVLink increases GPU-to-GPU communication bandwidth to 8 TB/s, enabling colossal clusters of up to 576 GPUs to work as a single, unified computer.

Efficiency Innovations:

Second-Generation Transformer Engine with support for the new NVFP4 (4-bit floating point) precision, which drastically reduces memory usage and dramatically improves inference efficiency (claiming up to 25X better energy efficiency for certain workloads over Hopper).

Head-to-Head Comparison Table of NVIDIA H200 and B200

The following table summarizes the main architectural and performance differences of the H200 vs. B200 GPUs:

Feature	NVIDIA H200	NVIDIA B200
Architecture	Hopper	Blackwell (Next-Generation)
Transistor Count	~80 Billion	~208 Billion
GPU Design	Monolithic Die	Dual-Die (connected by 10 TB/s NV-HBI)
Memory Capacity	141 GB HBM3e	192 GB HBM3e
Peak Memory Bandwidth	4.8 TB/s	~8.0 TB/s
Tensor Cores	4th Generation	5th Generation
New Precision Support	FP8 (with Transformer Engine)	FP4 (with Dual Transformer Engine)
NVLink Generation	4th Generation	5th Generation
Inter-GPU Bandwidth (NVLink)	Up to 900 GB/s	Up to 1.8 TB/s
Max Power (TDP)	~700W	~1000W
AI Performance (Estimated)	Up to 4 PetaFLOPS (FP8)	Up to 20 PetaFLOPS (FP4)
Best For	High-throughput inference, existing Hopper infrastructure upgrades, long-context LLMs.	Next-generation training and inference, ultra-long-context models, hyperscale AI factories.

When to Choose H200 or B200

Both H200 and B200 are powerful data center GPUs, but they serve slightly different needs. Understanding where each one of them excels helps enterprises balance performance, scalability, and cost-effectiveness. Here is when to choose each one of them:

1. When to Choose the NVIDIA H200

Handle memory-intensive workloads: Ideal for tasks requiring massive memory capacity and bandwidth, such as large language model (LLM) training and inference, scientific computing, and high-performance computing (HPC). This is enabled by its 141GB of HBM3e memory and 4.8TB/s bandwidth.
Upgrade existing Hopper systems: If you already use H100 systems, upgrading to H200 offers seamless compatibility without hardware redesign, providing an efficient path to higher performance.
Prefer a mature and stable platform: Built on the proven Hopper architecture, the H200 offers robust software, driver, and framework support (e.g., TensorFlow, PyTorch), minimizing deployment risks.

2. When to Choose the NVIDIA B200

Build cutting-edge AI infrastructure: As NVIDIA’s next-generation flagship GPU based on the revolutionary Blackwell architecture, the B200 is designed to power trillion-parameter generative AI models and push the limits of AI computing performance.
Maximize compute throughput and efficiency: With the new Blackwell architecture, the B200 delivers several times higher FP8 AI performance than the H200. Despite higher power consumption (up to 1000W), it achieves a significant leap in performance per watt.
Deploy large-scale, high-density compute clusters: The B200 is ideal for massive AI clusters, offering ultra-fast interconnects through fifth-generation NVLink and delivering extraordinary aggregate performance in systems like the DGX B200.

Wrapping Up

The NVIDIA H200 and B200 GPUs define a new era of progress in data center computing. The H200 delivers proven reliability for large-scale AI training and simulation workloads, while the B200 drives the next generation of generative AI with greater efficiency and scalability.

As enterprises continue building high-performance infrastructures, the role of trusted component suppliers becomes increasingly critical. UniBetter supports 3000+ clients worldwide, covering computing and storage, communications, new energy, automotive, and medical fields. Connect with UniBetter today for personalized support and partnership you can count on.

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