What’s Inside the Nvidia Blackwell Superchip?

What's Inside the Nvidia Blackwell Superchip?

Nvidia’s new chip, Blackwell, is set to revolutionize the landscape of AI. It boasts impressive performance and efficiency that will pave the way for a new era of artificial intelligence. But what exactly makes Blackwell so powerful and groundbreaking? Let’s dive in and explore the inner workings of this cutting-edge technology.

Nvidia Blackwell Architecture

At the heart of Blackwell is Nvidia’s latest Ampere architecture, tailored specifically for AI workloads. This architecture is exceptionally suited to the cutting-edge demands of artificial intelligence. It introduces a revolutionary Tensor Core design, which doubles the number of cores found in its predecessor, Volta.

This significant enhancement means that Blackwell is equipped for faster training and inference times, accelerating deep learning tasks and pushing the boundaries of AI technology. The B200, Nvidia’s flagship artificial intelligence product, embodies this advancement. Positioned at the core of an extensive ecosystem, including proprietary interconnect technology, the B200 represents the pinnacle of Nvidia’s AI innovation.

Nvidia plans to offer the B200 in various configurations, including as a vital component of the GB200 superchip. This superchip amalgamates two Blackwell GPUs with a Grace CPU, creating a powerhouse solution for the most demanding AI applications.

Nvidia Blackwell Core Features

Blackwell’s six revolutionary technologies collectively redefine the landscape of artificial intelligence training and real-time Large Language Model (LLM) inference, catering to models that scale up to an astonishing 10 trillion parameters. These include:

  • World’s Most Powerful Chip: The Blackwell-architecture GPUs, equipped with 208 billion transistors, are a testament to technological excellence. Manufactured through a custom-built 4NP TSMC process, these chips feature two-reticle limit GPU dies that are interconnected using a 10 TB/second chip-to-chip link, forming a single, unified GPU powerhouse.
  • Second-Generation Transformer Engine: With enhancements such as micro-tensor scaling support and NVIDIA’s advanced dynamic range management algorithms integrated into NVIDIA TensorRT™-LLM and the NeMo Megatron frameworks, the Blackwell architecture is poised to double the compute and model sizes. This upgrade brings new 4-bit floating point AI inference capabilities to the forefront of AI technology.
  • Fifth-Generation NVLink: The latest iteration of NVIDIA NVLink® propels performance into a new realm for multitrillion-parameter and mixture-of-experts AI models. Offering an unprecedented 1.8TB/s bidirectional throughput per GPU, this technology facilitates seamless, high-speed communication among up to 576 GPUs, enabling the most complex LLMs to operate efficiently.
  • RAS Engine: Embedded within Blackwell-powered GPUs, the RAS engine is dedicated to reliability, availability, and serviceability. The immersion of AI-based preventative maintenance capabilities at the chip level enables diagnostics and forecasts reliability issues. This innovation is key to maximizing system uptime and bolstering resiliency for massive-scale AI deployments, allowing for uninterrupted operation for extended periods and reducing operating costs.
  • Secure AI: In an era where data privacy is paramount, the advanced confidential computing capabilities of the Blackwell architecture ensure the protection of AI models and customer data without sacrificing performance. Support for new native interface encryption protocols becomes crucial in privacy-sensitive industries such as healthcare and financial services.
  • Decompression Engine: Addressing the critical need for speed in data analytics and science, the dedicated decompression engine accelerates database queries by supporting the latest formats. This enhancement promises to significantly boost performance in data processing, a sector where companies invest tens of billions of dollars annually, with a growing tendency towards GPU acceleration.

Performance Enhancements with Blackwell

Nvidia’s Blackwell chip marks a significant leap in the field of artificial intelligence training and inference capabilities. According to Nvidia, Blackwell’s performance in training AI models is a staggering 2.5 times greater than its predecessor, Hopper.

This metric is especially notable considering that training is an immensely computationally intensive process, fundamental to the development of AI by enabling the model to learn from data and thus, build intelligence. This performance enhancement is crucial for companies looking to develop in-house artificial intelligence tools, highlighting Nvidia’s pivotal role in the AI semiconductor market.

Furthermore, when it comes to reasoning — a critical ability for AI to make decisions based on learned information — Nvidia reports that Blackwell outperforms the former architecture by an impressive fivefold. This leap in reasoning performance is a testament to the sophisticated engineering behind Blackwell, positioning it as a leader in the AI technology space.

Perhaps most strikingly, Nvidia has indicated that, when integrated into a module comprising dozens of Blackwell chips, the collective performance of this new product line could skyrocket by up to 25 times compared to current capabilities. This potential for vast performance improvement is not just a theoretical advantage; it has the practical effect of potentially slashing operating costs for large data centers.

This efficiency gain is a compelling reason for customers to consider upgrading to Blackwell, underscoring Nvidia’s commitment to driving revolutionary advancements in AI technology.

Who Makes Blackwell Chips?

The manufacturing of Nvidia’s Blackwell chips showcases a sophisticated collaboration of technology and precision engineering. The Blackwell architecture GPUs, which form the foundation of this innovative technology, are composed of a staggering 208 billion transistors. These GPUs are brought to life using a highly specialized 4 nm process developed by TSMC (Taiwan Semiconductor Manufacturing Company), a leader in semiconductor fabrication. Although TSMC has introduced a more advanced 3 nm process for other applications, the decision to use the 4 nm process for Blackwell suggests a tailored approach to achieve the optimal balance between performance and power efficiency for AI tasks.

At Nvidia’s GTC (GPU Technology Conference), it was revealed that Heshuo, alongside a subsidiary of the renowned electronics manufacturer Foxconn, has entered the limelight with plans to sell products integrated with 36 and 72 GB200 superchips, respectively. These partnerships not only highlight the broad industrial support for Nvidia’s Blackwell chips but also underline the extensive ecosystem Nvidia is fostering, leveraging the capabilities of its Blackwell technology to push the boundaries of AI further.

The Impact of Blackwell on AI Advancements

Nvidia’s launch of the Blackwell chips represents a monumental shift in the capabilities and expectations surrounding artificial intelligence. CEO Hwang In-hoon succinctly addressed one of the primary challenges within AI development: the necessity for larger, more complex models that require significantly more powerful GPUs. By incorporating Blackwell chips into products like the GB200 NVL72, Nvidia is squarely addressing this demand, enabling the creation of models that are more comprehensive and intricate than ever before.

These advanced models transcend beyond the traditional realm of text-based data, incorporating a vast array of content types including images, charts, videos, and more. This breadth of data allows for more nuanced and sophisticated AI applications, capable of understanding and processing a richer tapestry of human interaction. Furthermore, the increase in computational parameters supported by these chips means that models can now accommodate the kind of complexity that closely mimics human cognitive processes.

Perhaps most groundbreaking is Hwang In-hoon’s assertion that superchips like the GB200 will usher in the era of the “trillion parameter model”. This level of computational might not only marks a significant technical milestone but also dramatically expands the potential scope of AI’s application – opening doors to solving more complex problems, creating more accurate predictive models, and realizing AI ambitions that were previously considered beyond reach. Thus, the Blackwell chip does not just signify progress in hardware efficiency; it heralds a new epoch in the evolution and capability of artificial intelligence.

AGI Will Come Within The Next 5 Years

Nvidia CEO Jensen Huang has expressed a belief that some form of artificial general intelligence (AGI) will emerge within the next five years. This timeframe, while ambitious, highlights the rapid pace at which AI technology is advancing. AGI, a concept that refers to a machine’s ability to understand or learn any intellectual task that a human being can, remains a largely theoretical idea without a universally accepted definition.

To bridge this gap, Huang emphasizes the importance of establishing a clear definition for AGI, accompanied by standardized tests. These tests would serve to demonstrate and quantify a software program’s “intelligence”, offering a measurable way to gauge progress towards achieving AGI.

Huang’s perspective underscores a significant challenge within the artificial intelligence community — transitioning from the theoretical to the practical realization of AGI, a milestone that would fundamentally alter the landscape of technology and society.

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