NVIDIA GB200 AI Chip : Architecture, Specifications, NVL72 Performance & Its Applications

The NVIDIA GB200 functions as a unified high-performance computing system by combining a Grace CPU and two Blackwell GPUs. These components are interconnected via high-bandwidth NVLink-C2C, enabling seamless data transfer and scalability. The system uses liquid cooling to maintain peak performance during intensive AI and HPC workloads.

What is NVIDIA GB200?

The NVIDIA GB200 stands out as a highly integrated and powerful super chip built on the Blackwell architecture. This module easily combines one NVIDIA Grace CPU and two NVIDIA B200 Tensor Core GPUs in a single package to deliver extraordinary AI performance. NVLink-C2C interconnects these CPUs and GPUs, providing a 900GB bandwidth that doubles the computing power of the GPU memory.

Engineers designed this module primarily for demanding AI and high-performance computing workloads. It is a significant component within the GB200 NVL72 larger system, which is a liquid-cooled, NVL72 rack system that trains and inferences LLMs or large language models.

How does the NVIDIA GB200 Work?

The NVIDIA GB200 works as a unified GPU to handle complex HPC and AI workloads. It can be achieved through a combination of both Grace CPU & Blackwell GPUs, which are interconnected through gh-bandwidth NVLink-C2C & leveraging a liquid cooling system. So this NVIDIA Blackwell architecture allows significant performance gains & improvements in energy efficiency as compared to earlier generations.

The GB200 combines a Grace CPU, including two Blackwell GPUs to work as a single and powerful computing unit. This module uses NVLink-C2C to allow fast communication between the CPU, GPUs, and also between various Unified GPU systems. The GB200 module needs liquid cooling infrastructure from a Cooling Distribution Unit (CDU) to adjust cooling output dynamically to maintain the best temperatures.

The NVIDIA GB200 system architecture allows scaling up to handle large AI models, which are demonstrated through the NVL72 configuration using 72 Blackwell GPUs. NVIDIA evaluates the GB200  to provide 25x best energy efficiency, mainly for trillion-parameter AI models as compared to the H100 air-cooled system.

Key Specifications:

The specifications of the NVIDIA GB200 include the following.

• A single GB200 superchip includes one Grace CPU and two Blackwell GPUs.
• It uses NVLink to form a unified memory domain to enhance communication & performance.
• The Blackwell GPUs of this chip are built with a 5 nm process, and it provide improved performance above earlier generations.
• The GB200 chip provides a large amount of HBM3e memory for GPUs & LPDDR5X memory for CPUs, including BWs for optimized performance.

Configurations:

NVIDIA GB200 is available in three configurations, which are explained below.

• The GB200 configuration is a rack-scale system with 72 Blackwell GPUs and 36 Grace CPUs, which are interconnected through NVLink. So it provides 1.8 TB/s GPU-to-GPU interconnect totally.
• The GB200 NVL2 configuration has two Blackwell GPUs and two Grace CPUs.
• The GB200 Superchip configuration is the basic element in the NVL72, which includes one Grace CPU & two Blackwell GPUs.

NVIDIA GB200 Vs GB200 NVL72

The difference between NVIDIA GB200 Vs GB200 NVL72 includes the following.

NVIDIA GB200	GB200 NVL72
The NVIDIA GB200 is a superchip that combines a Grace CPU and two B200 GPUs.	The GB200 NVL72 chip is a rack-scale system that integrates 72 GB200 chips.
It is designed to provide high-performance, based AI training & inference at an available scale.	It provides enormous performance for huge LLM workloads, performs as a single and powerful AI building block.
Offers a significant performance boost over earlier generations, like the H100, particularly for LLM inference GPUs.	Offers huge memory capacity & high-speed interconnect for compound AI models.
It focuses on performance for each chip.	It focuses on performance at the level.
This chip is not as expensive as compared to NVL72.	It is more expensive because of its scale & performance abilities.
GB200 is appropriate for a variety of AI tasks.	It is optimized mainly for the demanding LLM workloads.

Comparison Table

The comparison between A100, H100, and GB200 are discussed below.

Feature	A100	H100	GB200
Architecture	Ampere	Hopper	Blackwell Rack-Scale Architecture
Max TFLOPS (FP32)	19.5	67	5,760 (NVL72 Total)
Memory	40GB	HBM280GB	192GB – HBM3e memory
CPU Integration	No	No	36 Grace CPUs
Use Case	AI/HPC	LLMs, AI Models	Trillion-parameter LLM
Cooling	Air	Air	Rack-scale liquid-cooled system

NVIDIA GB200 Architecture

NVIDIA GB200 architecture includes different components, which are explained below.

Blackwell GPUs

The GB200 chip features two Blackwell GPUs, specifically engineered for AI workloads and high-performance computing. As NVIDIA’s latest generation GPU architecture, it emphasizes scalability, versatility, and efficiency, particularly for large language models and generative AI. Key attributes include a staggering 208 billion transistor count, a custom-built TSMC 4NP process, and a chip-to-chip interconnect speed of 10 terabytes per second.

You can integrate the Blackwell GPU into workstation and server solutions like the RTX PRO 6000, which enhances AI processing power for professional workflows. Each GB200 GPU incorporates an NVIDIA Grace CPU, delivering a robust processing unit for comprehensive system control and data management. Designed to provide groundbreaking compute performance, some Blackwell systems achieve up to 20 petaFLOPS on a single chip, leveraging NexGenCloud technology.

NVLink

The NVLink is used to interconnect Grace & Blackwell chips by enabling low-latency and high-speed communication between the GPUs, CPU, and between several GPUs in the system. The NVIDIA GB200 GPU uses NVLink fifth-generation technology to unite its GPUs by allowing huge scalability & high-speed communication for HPC and AI workloads.

The GB200 specially incorporates 72 Blackwell GPUs within a single NVLink domain, by providing an enormous shared memory space with above 1.8 TB/s of bidirectional BW for each GPU. This improved NVLink interconnect is essential for training LLMs (large language models) with trillions of parameters, allowing real-time execution.

NVL72 System

The GB200 is a core component of the NVL72, a rack-scale system that combines 36 GB200s (72 Blackwell GPUs and 36 Grace CPUs) into a single, unified computing platform.

The NVL72 is a liquid-cooled, rack-scale system designed by NVIDIA to power the most demanding AI and high-performance computing workloads. It combines 36 NVIDIA GB200 “Grace Blackwell” Superchips, each containing a Grace CPU and two Blackwell GPUs, into a single 48U rack. This configuration comprises a total of 72 Blackwell GPUs and 36 Grace CPUs interconnected via NVIDIA’s NVLink network, delivering a massive 1.4 exaflops of AI performance and 14 TB of memory within a single rack.

Liquid Cooling

The NVL72 uses liquid cooling for very efficient heat dissipation by its high-production components, which allows higher energy efficiency. This is a combination of in-rack CDUs (coolant distribution units) and direct-to-chip liquid cooling, which allows for greater energy efficiency and compute density within data centers.

Liquid cooling allows the GB200 to provide significant staging gains, mainly in high-performance computing and AI workloads, while decreasing footprint and energy consumption as compared to fixed air-cooled systems.

LLM Inference

The GB200 and NVL72 primarily accelerate LLM inference, with the NVL72 delivering up to a 30x performance increase compared to an equivalent number of NVIDIA H100 GPUs. This impressive boost results from a combination of hardware advancements, including the fifth-generation NVLink, the second-generation transformer engine, and software optimizations.

AI Training GPU

The GB200 chip provides significant improvements in the execution of AI training tasks, including an FP8 precision and a faster second-generation transformer engine. This chip is a powerful platform that is designed mainly for large-scale AI training & inference, mainly for LLMs (large language models). It influences the Blackwell GPU architecture & the Grace CPU provides significant performance developments over earlier generations. Its key features mainly include a high-speed NVLink interconnect, a second-generation transformer engine & the capacity to scale to enormous configurations.

Memory

The GB200 provides a large combined memory space that is allowable to both the Blackwell GPUs and the Grace CPU by facilitating efficient data sharing & processing. The NVIDIA GB200 is a combination of GPU and CPU memory, where every Blackwell GPU is equipped with 192GB HBM3e memory. Alternatively, the Grace CPU uses 512GB LPDDR5 memory.

These are connected through NVLink-C2C, which provides 896GB of total unified memory and is accessible to all devices. The GB200 NVL72 configuration connects 36 Grace CPUs, including 72 Blackwell GPUs, which boast up to LPDDR5X 17 TB of memory on the CPU side & up to HBM3e 13.4 TB of memory on the GPU side.

Performance Highlights:

• The GB200 chip is designed to extensively speed up LLM inference, including the GB200 NVL72, to provide 30x faster instantaneous trillion-parameter LLM inference.
• GB200 offers 5,760 TFLOPS of FP32 compute execution.
• The GB200 provides GPU memory bandwidth up to 576 TB/sec.
• The GB200 is a power-intensive system that needs liquid cooling solutions for best production.
• Arranging the GB200 needs considerations of data center infrastructure like power, networking & cooling.

Software Stack Compatibility

NVIDIA has fully integrated the GB200 into its robust AI software ecosystem, ensuring compatibility with the most widely used machine learning and deep learning frameworks. This chip optimizes the acceleration of training and inference tasks, utilizing both proprietary and open-source tools.

Supported Software and Frameworks:

• CUDA Toolkit: GB200 supports CUDA 12+ with full backward compatibility, enabling accelerated parallel computing.
• cuDNN: Optimized support for deep neural networks, particularly transformer-based LLMs.
• TensorRT: Enhances inference performance for AI models running on GB200.
• NVIDIA NeMo: Prebuilt models and training pipelines for large language models (LLMs) are optimized for the Grace-Blackwell architecture.
• Triton Inference Server: Enables model serving and scaling across multiple GPUs in the NVL72 rack system.
• ML Frameworks:
  • TensorFlow
  • PyTorch
  • JAX
  • ONNX Runtime

This tight integration ensures developers can scale LLMs across thousands of GPUs without rewriting codebases, making the GB200 suitable for real-world deployment in large-scale AI infrastructure.

Energy Efficiency Metrics

NVIDIA GB200 emphasizes energy efficiency as a core design principle, especially in the NVL72 configuration. Compared to the H100 air-cooled system, the GB200 NVL72 provides:

• 30x faster LLM inference with
• 25x better energy efficiency

Key Metrics:

Metric	H100 (Air Cooled)	GB200 NVL72 (Liquid Cooled)
AI Inference Performance	1x	Up to 30x
Power Efficiency (TFLOPS/W)	Baseline	Up to 25x better
Cooling Type	Air	Liquid (Direct-to-Chip + CDU)
GPU-to-GPU Interconnect	~900 GB/s	1.8 TB/s total per rack

By leveraging HBM3e memory, NVLink-C2C, and Grace CPUs, NVIDIA has significantly reduced the power needed per AI operation. These improvements make GB200 ideal for sustainable AI data centers aiming to reduce their carbon footprint while scaling compute workloads.

Case Study or Use Case Mentions

While NVIDIA has not officially disclosed all clients adopting the GB200, early adopters and use cases can be inferred based on existing partnerships and industry interest in LLMs and HPC applications.

Likely Use Cases and Organizations:

• OpenAI: Likely to leverage GB200 chips for training GPT-like models with trillions of parameters.
• Google DeepMind: For running complex simulations and next-gen generative AI tasks.
• Meta: To power the training of LLaMA models and AI-based content moderation tools.
• Amazon AWS & Microsoft Azure: Expected to include GB200 instances in cloud offerings via NVIDIA DGX Cloud.
• NVIDIA Morpheus: GB200 is expected to supercharge Morpheus for real-time AI-based cybersecurity.

These organizations require massive GPU compute density, low latency, and high memory throughput, making the Grace-Blackwell architecture a strategic fit.

Future Trends and Roadmap

The launch of the GB200 and NVL72 platforms marks a transition point in AI compute, moving from general-purpose GPUs to highly specialized, hybrid architectures for AI.

What’s Next?

• Multi-Die GPUs: Following the chiplet approach of GB200, NVIDIA may explore modular, customizable AI accelerators tailored to domain-specific models (e.g., GenAI, robotics, genomics).
• Next-Gen NVLink: Further evolution of NVLink could bring photonics or optical interconnects to overcome electrical limitations at exascale computing levels.
• Quantum-AI Integration: Grace Blackwell’s superchip may integrate or complement emerging quantum computing co-processors in the future.
• Edge-optimized versions: Scaled-down Grace-Blackwell superchip derivatives could appear in AI edge servers, autonomous vehicles, and robotics platforms.

Key Takeaway:

The GB200 isn’t just a performance leap—it sets the foundation for NVIDIA’s roadmap toward trillion-parameter foundation models, exascale supercomputing, and energy-aware.

Advantages

The advantages of the NVIDIA GB200 include the following.

• The GB200 module provides up to a 30x performance increase as compared to a similar number of NVIDIA H100 Tensor Core GPUs, mainly for LLM inference.
• It delivers 30X faster real-time LLM (large language model) inference, supercharges AI training, & delivers advanced performance.
• It has NVLink-C2C, high-bandwidth memory act & dedicated relaxation engines within the NVIDIA Blackwell architecture.
• The GB200 module’s computing power will be six times greater & particularly for multimodal tasks.
• These are chosen by leading tech giants for their outstanding act in gaming, machine learning, and AI.
• It provides extensive gains, better cost efficiency, and improved energy efficiency.
• The GB200 incorporates advanced features.
• It is compatible with a wide range of HPC and AI applications.
• The liquid-cooled rack-scale system of the GB200 NVL72 allows for high-density deployments to handle the power requirements.
• It provides considerable efficiency gains to handle large AI models and complex workloads.

Disadvantages

The disadvantages of the NVIDIA GB200 include the following.

• It has a complex design.
• These are expensive.
• High power consumption.
• It can create overheating issues, UQD leakage & copper wire yield issues.
• Software bugs & inter-chip connectivity issues.

Applications

The applications of NVIDIA GB200 include the following.

• The NVIDIA GB200 accelerates different AI & HPC workloads like large language model training, data processing, and vector database search.
• The GB200 needs liquid cooling infrastructure like a Cooling Distribution Unit (CDU) that adjusts cooling output dynamically.
• The GB200-powered Morpheus provides AI-powered cybersecurity solutions.
• It is used in autonomous vehicles for in-vehicle computing.
• The Aerial platform of telecommunications applications uses it.
• This module primarily targets data centers, and its capabilities enhance gaming through DLSS and RTX technologies.
• It plays a crucial role in NVIDIA’s Omniverse platform, enabling the creation and simulation of digital twins.
• The NVIDIA GB200 NVL72 module design represents a major milestone in the development of current high compute-density data centers. This contribution speeds up the acceptance of energy-efficient compute-density platforms within the data center.
• To address the pressing challenges of training and serving growing AI models and high GPU-to-GPU communication.

This is an overview of NVIDIA GB200, developed to power the next generation of HPC and AI workloads. So it mainly targets the training & inference of LLMs with thousands of parameters. This chip offers energy efficiency gains and significant improvements as compared to earlier generations. Here is a question for you: What is NVIDIA?