NVIDIA Reveals Most Powerful Chip for AI: Blackwell Beast

B200 blows the H100 out of the water. B200 boasts 20 petaflops of AI compute compared to H100's 4 petaflops (at FP4 precision). That's a 4x improvement.

Introduction

In a monumental leap forward for the field of artificial intelligence, NVIDIA has unleashed its latest innovation: the Blackwell Beast.

Representing a significant departure from conventional chips, the Blackwell Beast heralds the dawn of a new era in AI computing, such as offering unparalleled performance and scalability for trillion-parameter scale generative AI models.

Powerhouse for AI: This chip is designed specifically for artificial intelligence tasks, and Nvidia claims it can handle models with up to 27 trillion parameters . That’s a massive leap in processing power.

Intricate Design: The B200 GPU uses a unique architecture with a high number of internal connections, which contributes to its power. 

Performance Enhancements

At the heart of this groundbreaking advancement lies the B200 GPU, a technological marvel boasting up to 20 petaflops of FP4 horsepower fueled by an astonishing 208 billion transistors.

Moreover, this raw computational power is further amplified by the GB200, which integrates two B200 GPUs alongside a Grace CPU to deliver a staggering 30x improvement in LLM inference.

Its workload performance compared to its predecessors, all while operating at 25x greater efficiency than the H100.

Energy and Cost Efficiency

The Blackwell Beast isn’t just about raw power—it’s also about efficiency. Training a 1.8 trillion parameter model now demands a mere 2,000 Blackwell GPUs and 4 megawatts of power, a fraction of the resources previously required by the Hopper GPUs, which consumed 15 megawatts for the same task.

This remarkable improvement in energy and cost efficiency represents a seismic shift in the landscape of AI computing, making large-scale AI projects more accessible and sustainable than ever before.

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Benchmark Performance

In head-to-head comparisons against its predecessors, the Blackwell Beast reigns supreme.

On a GPT-3 benchmark, the GB200 outshines the H100 by a staggering margin, offering 7x greater performance and 4x faster training speeds.

These results underscore the Blackwell Beast’s dominance in the realm of AI computing, setting a new standard for performance and efficiency in the industry.

Key Technical Improvements

Beyond its sheer computational prowess, the Blackwell Beast introduces a host of key technical advancements designed to push the boundaries of AI research and development.

This includes a second-gen transformer engine that doubles compute, bandwidth, and model size, as well as a new NVLink switch that facilitates enhanced GPU communication, allowing for seamless integration and collaboration across a network of 576 GPUs with a blistering 1.8 TB/s bandwidth.

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Large-Scale AI Systems

To accommodate the growing demands of large-scale AI training and inference tasks, NVIDIA has introduced the GB200 NVL72 racks, each comprising 36 CPUs and 72 GPUs.

These formidable systems are capable of delivering up to 1.4 exaflops of inference performance, providing researchers and developers with the tools they need to tackle the most complex and ambitious AI projects with confidence.

Here’s a table summarizing the key features and improvements of NVIDIA’s Blackwell Beast:

FeatureDescription
Performance Enhancements
B200 GPUUp to 20 petaflops of FP4 horsepower with 208 billion transistors
GB200Combines two B200 GPUs and a Grace CPU for 30x LLM inference workload performance, 25x efficiency over H100
Energy and Cost Efficiency
Training EfficiencyTraining a 1.8 trillion parameter model now takes 2,000 Blackwell GPUs and 4 megawatts, compared to 8,000 Hopper GPUs and 15 megawatts previously
Benchmark Performance
GB200 vs. H1007x more performant on GPT-3 benchmark, 4x faster training speed
Key Technical Improvements
Second-gen Transformer EngineDoubles compute, bandwidth, and model size
NVLink SwitchEnhanced GPU communication, allowing 576 GPUs to connect with 1.8 TB/s bandwidth
Large-Scale AI Systems
GB200 NVL72 RacksCombine 36 CPUs and 72 GPUs for up to 1.4 exaflops of inference performance
NVIDIA GTC 2024 (0:00 – 6:00)

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Conclusion

With the unveiling of the Blackwell Beast, NVIDIA has once again raised the bar for AI computing, ushering in a new era of unprecedented performance, efficiency, and scalability.

From its revolutionary architecture to its groundbreaking technical innovations, the Blackwell Beast represents the pinnacle of AI engineering, empowering researchers and developers to unlock new frontiers in artificial intelligence and reshape the future of technology as we know it. With its unparalleled processing capabilities, this formidable chip not only accelerates AI research but also propels industries towards unprecedented efficiency and scalability. Moreover, by pushing the boundaries of computational power, the Blackwell Beast heralds a new era of innovation, fostering the development of AI-driven solutions that promise to revolutionize diverse sectors.

As we embark on this exciting journey towards trillion-parameter scale generative AI, one thing is clear: the age of the Blackwell Beast has arrived, and the possibilities are limitless.

Kumar Priyadarshi
Kumar Priyadarshi

Kumar Priyadarshi is a prominent figure in the world of technology and semiconductors. With a deep passion for innovation and a keen understanding of the intricacies of the semiconductor industry, Kumar has established himself as a thought leader and expert in the field. He is the founder of Techovedas, India’s first semiconductor and AI tech media company, where he shares insights, analysis, and trends related to the semiconductor and AI industries.

Kumar Joined IISER Pune after qualifying IIT-JEE in 2012. In his 5th year, he travelled to Singapore for his master’s thesis which yielded a Research Paper in ACS Nano. Kumar Joined Global Foundries as a process Engineer in Singapore working at 40 nm Process node. He couldn’t find joy working in the fab and moved to India. Working as a scientist at IIT Bombay as Senior Scientist, Kumar Led the team which built India’s 1st Memory Chip with Semiconductor Lab (SCL)

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