NVIDIA HGX B200 (8-GPU) Platform

Description

The NVIDIA HGX B200 is the latest AI accelerator platform from NVIDIA, built on the revolutionary Blackwell architecture. It brings data center infrastructure into a new era of Generative AI and Large Language Model (LLM) training.
With eight sixth-generation Blackwell GPUs (SXM6) and fifth-generation NVLink technology, the HGX B200 delivers up to 15x faster performance than its predecessor, the NVIDIA H100, setting a new benchmark for power, energy efficiency, and scalability in the world of artificial intelligence.

Key Features

• Up to 15x faster inference performance for LLMs compared to H100

• 3x faster training performance for large AI models

• 12x better energy efficiency and lower total cost of ownership (TCO)

• Supports models with over 1.8 trillion parameters (e.g., GPT-MoE 1.8T)

• 1.8 TB/s GPU-to-GPU bandwidth via NVLink Gen 5

• 1.4 TB of HBM3E memory with 62 TB/s bandwidth

• Support for FP4, FP8, and BF16 precision for optimal performance and accuracy

• Compatible with NVIDIA InfiniBand and BlueField-3 DPU networks

Blackwell Architecture – The Core of HGX B200

At the heart of this platform lies the Blackwell architecture, NVIDIA’s next-generation GPU design created to support trillion-parameter models and real-time AI workloads.
Blackwell features the second-generation Transformer Engine, which introduces new numerical formats such as FP4, enabling massive AI models to train faster while maintaining computational accuracy.

Each GPU in the HGX B200 platform includes:

• 180 GB of HBM3E memory

• 8 TB/s memory bandwidth

• Up to 9 PFLOPS of FP8 performance

• Configurable TDP up to 1000W for HPC and AI workloads

Through NVLink Gen 5, GPUs are interconnected with 14.4 TB/s total GPU-to-GPU bandwidth, enabling ultra-low latency communication and near-linear scalability for large AI clusters.

Technical Specifications

Performance in LLM and Generative AI Workloads

The HGX B200 platform is purpose-built for Large Language Model (LLM) processing and Generative AI applications.
According to internal NVIDIA benchmarks, the HGX B200 achieves:

• Up to 15x faster inference on GPT-MoE models compared to H100

• Up to 3x faster training performance for equivalent workloads

• Over 12x reduction in power consumption during inference

With support for the NVIDIA NeMo™ and TensorRT-LLM frameworks, developers can efficiently deploy, train, and scale advanced AI models at full data center scale.

Energy Efficiency and Sustainability

The NVIDIA HGX B200 is part of NVIDIA’s ongoing commitment to sustainable computing and energy-efficient data centers.
Thanks to optimized design across silicon, memory, and interconnects, the HGX B200 delivers:

• 12x higher energy efficiency compared to the previous generation

• Lower cooling and power requirements for data center operations

• Reduced total cost of ownership (TCO) in large-scale deployments

These advancements make it the ideal choice for organizations aiming to combine performance, efficiency, and long-term sustainability in their AI infrastructure.

Compatibility and Flexibility

The HGX B200 comes in the SXM6 form factor and is fully compatible with the latest Lenovo ThinkSystem SR680a V3 (air-cooled) and SR780a V3 (liquid-cooled) servers.

It supports NVIDIA’s complete software ecosystem, including:

• CUDA Toolkit

• NVIDIA AI Enterprise

• Magnum IO

• HPC SDK

This ensures seamless deployment across HPC, cloud AI, and enterprise data center environments.

NVIDIA HGX B200 VS NVIDIA GB200

A direct comparison between NVIDIA HGX B200 and GB200  shows how each serves different AI workloads — from scalable data center nodes to full AI supercomputing systems.

• Unmatched scalability for LLM and Generative AI projects

• Superior performance in AI training and inference

• Optimized energy consumption and reduced operational costs

• Backed by NVIDIA’s enterprise-grade reliability and security

• Fully compatible with servers from Lenovo, Dell, HPE, and Supermicro

Conclusion

The NVIDIA HGX B200 is more than a hardware platform — it is the foundation for the next generation of AI-driven data centers.
Combining exceptional compute power, remarkable energy efficiency, and seamless scalability, the HGX B200 is the ultimate choice for research institutions, data centers, and enterprises building the future of artificial intelligence.

Frequently Asked Questions (FAQ) – NVIDIA HGX B200

Can HGX B200 run multi-instance GPUs (MIG)?

Yes, HGX B200 supports up to 7 MIG instances per GPU, enabling multiple isolated workloads on a single GPU for better resource utilization.

How many GPUs and how much memory does it support?

HGX B200 supports 8 NVIDIA Blackwell GPUs with up to 1.4 TB HBM3E memory and 1.8 TB/s NVLink bandwidth between GPUs via NVSwitch.

What server options support HGX B200?

HGX B200 is compatible with Lenovo ThinkSystem servers including:

• SR780a V3 (liquid-cooled)

• SR680a V3 (air-cooled)

• Other ThinkSystem SR, ST, SD series servers certified under NVIDIA ServerProven™ program.

How does HGX B200 compare to the previous H100 generation?

HGX B200 offers up to 15X faster real-time LLM inference, 3X faster training, and 12X lower energy consumption and total cost of ownership (TCO) compared to the NVIDIA H100.

Are there any new media or decompression features?

Yes, HGX B200 includes a Decompression Engine, 7 NVDEC decoders, and 7 nvJPEG decoders, supporting accelerated media processing workloads.