Conversation with Groq CEO Jonathan Ross

In a conversation with Groq CEO Jonathan Ross, he discusses the rapid growth of his company, the importance of developers in building applications, and the unique origin story of his entrepreneurial journey. Ross, a high school dropout who later contributed to Google's TPU project, shares insights on the technical considerations of Groq versus Nvidia, the software stack, and the impressive data points that set Groq apart. He highlights the company's focus on compiler development, the design decisions behind their chips, and the strategic choice to use older technology to achieve significant performance advantages. Ross also addresses the challenges of team building in Silicon Valley, the future of AI, and the potential impact on jobs, comparing large language models to telescopes that reveal the vastness of intelligence.

Takeaways

• 🚀 Groq CEO Jonathan Ross discusses the rapid growth of their developer community, reaching 75,000 developers in about 30 days, compared to Nvidia's seven years to reach 100,000 developers.
• 🌟 Ross highlights the importance of developers in building applications and their multiplicative effect on the total number of users.
• 🎓 The transcript reveals Ross's unconventional educational and entrepreneurial journey, from being a high school dropout to starting a billion-dollar company.
• ⚙️ Ross shares his experience at Google, where he worked on ad testing systems and contributed to the development of Google's custom silicon, the TPU, during his '20% time'.
• 🔍 The TPU project aimed to solve the problem of affordability in deploying machine learning models, which was a significant hurdle for Google's speech recognition technology.
• 💡 Ross's insight into building scaled inference systems, inspired by AlphaGo's TPU performance, led to Groq's focus on inference rather than training, which has become a significant market opportunity.
• 📈 Groq's design decisions, including the use of an older 14nm technology and a focus on compiler development, allowed them to create a more cost-effective and scalable solution for AI inference.
• 🏆 Groq's performance claims are bold, stating they are 5 to 10 times faster than GPUs in certain AI inference tasks, positioning them as a strong contender in the market.
• 🤖 Ross discusses the challenges of latency in AI applications and how reducing latency to under 300 milliseconds is crucial for user engagement and revenue optimization.
• 🌐 He emphasizes the importance of being able to quickly adapt to new AI models in the inference market, which Groq's system is designed to accommodate.
• 💼 The transcript touches on the economic implications of AI, suggesting that the cost of running AI applications should decrease, enabling startups to flourish with less investment in infrastructure.
• ⛓ Ross addresses concerns about AI's impact on jobs and the future, likening the current AI revolution to the historical moment when Galileo's telescope expanded our understanding of the universe.

Q & A

How many developers does Groq have, and how does this compare to Nvidia's growth?

Groq has 75,000 developers, which is a significant milestone considering it was achieved in about 30 days after launching their developer console. In comparison, it took Nvidia seven years to reach 100,000 developers.

What is the significance of the number of developers for a tech company like Groq?

Developers are crucial because they build applications, and each developer has a multiplicative effect on the total number of users a company can have. The more developers, the more applications are created, leading to a broader user base.

What was Jonathan Ross's educational background before he started his entrepreneurial journey?

Jonathan Ross is a high school dropout who later attended Hunter College and then transferred to NYU, where he took PhD courses as an undergrad but did not complete the program. Despite not having a high school diploma or an undergrad degree, his educational journey and the connections he made were instrumental in his career.

How did Jonathan Ross end up at Google?

Jonathan Ross was referred to Google by someone he met at an event, who recognized him from their time at NYU. Despite not having a degree, the connection led to an opportunity at Google, where he worked on ads testing and contributed to the development of Google's custom silicon, the TPU.

What problem did Jonathan Ross aim to solve with the development of the TPU?

The TPU was designed to address the issue of affordability and scalability in deploying machine learning models. The speech team at Google had developed a model that outperformed humans in speech recognition but was too expensive to put into production. The TPU aimed to make such models economically viable for widespread use.

How does Groq's approach to chip design differ from Nvidia's?

Groq focuses on a compiler-first approach, which is more scalable and does not require hand-optimizing kernels for each application. This contrasts with Nvidia's approach, which relies on low-level kernel writing and assembly, which is more labor-intensive and less scalable.

What is the significance of latency in AI applications?

Latency is critical for user engagement and the overall user experience in AI applications. Ideally, responses should be returned in under 300 milliseconds to maintain user satisfaction and engagement. Higher latency leads to decreased user interaction and a poor user experience.

How does Groq's chip architecture cater to the needs of inference in AI applications?

Groq's chip architecture is designed to provide high performance in inference, focusing on compute capabilities and scalability. It is built to handle hundreds or thousands of chips working together, similar to how they were used in AlphaGo, to provide fast and efficient inference.

What is the current market share of inference in the AI industry, and what does the future look like?

As of the latest Nvidia earnings, inference makes up about 40% of the market. It is expected to grow rapidly, possibly reaching between 90 to 95% of the market in the future, especially with the advent of open-source models that are freely available for deployment.

How does Groq's team-building strategy in Silicon Valley differ from other tech companies?

Groq focuses on hiring experienced engineers who know how to ship products on time and let them learn AI. This approach is based on the belief that these engineers can quickly acquire AI skills, whereas it would be more challenging for AI researchers to gain decades of experience in deploying production code.

What is Jonathan Ross's perspective on the future of AI and its impact on jobs and society?

Jonathan Ross views large language models as the 'telescope for the mind,' suggesting that as we become more accustomed to the vastness of intelligence, we will find our place within it without fear. He believes that the realization of the vastness of intelligence will lead to appreciation and understanding, much like how our perception of the universe changed with the invention of the telescope.

Outlines

00:00

📈 Introduction and Developer Growth

The speaker expresses excitement about the event and introduces Jonathan, highlighting his unique origin story as a high school dropout who founded a billion-dollar company. The discussion focuses on the rapid growth of developers using their platform, reaching 75,000 in 30 days, compared to Nvidia's seven years to reach 100,000. The importance of developers is emphasized, as they are key to building applications and increasing the user base exponentially. The speaker also reflects on Jonathan's journey, from working as a programmer to attending university classes and eventually joining Google, where he contributed to the development of TPU (Tensor Processing Unit).

05:00

🚀 TPU's Inception and Impact

The narrative delves into the challenges faced by Google with machine learning models being too costly to put into production. This led to Jeff Dean presenting the issue to leadership, highlighting the need for a cost-effective solution. The TPU project, initially an unofficial side project, leveraged matrix multiplication to accelerate AI tasks. Despite competition from other teams, the TPU team's innovative approach, including the use of a systolic array, proved successful. The speaker also discusses the decision to leave Google and the desire to build something from the ground up, leading to the founding of a new company.

10:02

🤖 Groq's Design Philosophy and Market Position

The speaker outlines Groq's strategic decisions, focusing on the need for a compiler rather than custom hardware due to the difficulty of programming AI chips. Groq's approach to building a scalable inference system is highlighted, drawing inspiration from the success of AlphaGo and the need for a system that could handle hundreds or thousands of chips. The comparison between Groq and Nvidia is discussed, with the speaker pointing out Nvidia's strengths in vertical integration and training, while Groq focuses on inference. The limitations of Nvidia's approach for inference tasks are also covered.

15:03

💼 Economic Implications and Market Strategy

The economic impact of AI on startups and the cost of computation are discussed, with the speaker emphasizing the need for a low-cost alternative to Nvidia's solutions. The speaker also addresses the technical aspects of Groq's chip design, opting for an older but more suitable technology to achieve significant performance advantages. The comparison between Groq's performance and Nvidia's B200 chip is made, with Groq demonstrating superior speed and cost-effectiveness. The importance of latency in user engagement is highlighted, with the speaker sharing experiences from working at Facebook and the push for faster response times.

20:03

🧠 The Future of AI and Inference

The speaker discusses the differences between training and inference in AI, emphasizing the need for a new chip architecture specifically designed for inference. The challenges of maintaining a leading position in both training and inference are explored, with the speaker suggesting that Nvidia may not maintain its dominance in inference. The shift in market demand from training to inference is highlighted, with predictions that inference will dominate the market in the coming years. The speaker also touches on the importance of being able to quickly adapt to new AI models and the advantages of Groq's approach in this context.

25:05

🌟 Team Building and the Future of AI

The challenges of building a team in Silicon Valley, especially when competing with major tech companies, are discussed. The speaker shares strategies for attracting and retaining talent, advocating for hiring experienced engineers who can learn AI rather than AI researchers without production experience. A recent partnership with Saudi Aramco is mentioned, emphasizing that the collaboration is complementary, not competitive, with tech giants. The speaker concludes with a perspective on the future of AI, likening large language models to telescopes that expand our understanding of intelligence, and suggesting that we will eventually appreciate our place within this vast intelligence landscape without fear.

Keywords

Developer Metrics

Developer metrics refer to the various quantitative measures used to assess the growth and engagement of a developer community. In the context of the video, Groq's CEO, Jonathan Ross, highlights that they have reached 75,000 developers in about 30 days since launching their developer console, which is a significant achievement compared to Nvidia's seven years to reach 100,000 developers. This metric is crucial as developers are responsible for building applications that drive user adoption and expansion of the technology.

High School Dropout

The term 'High School Dropout' is used in the video to describe Jonathan Ross's educational background before he became an entrepreneur. It is a significant part of his origin story, emphasizing the unconventional path he took to success. Ross did not complete high school but went on to work as a programmer and later attended university classes without formally enrolling. This keyword is used to illustrate that formal education is not the only pathway to innovation and entrepreneurial success.

Google

Google is mentioned as the company where Jonathan Ross worked and where he contributed to the development of the Tensor Processing Unit (TPU). Google is known for its innovative work culture and the '20% time' policy, which allowed Ross to work on the TPU as a side project. The company's approach to innovation and its role in Ross's career is highlighted to show how large tech companies can serve as incubators for groundbreaking technologies.

Tensor Processing Unit (TPU)

The Tensor Processing Unit (TPU) is a custom silicon chip developed by Google for machine learning applications. In the video, Ross discusses how the TPU was initially a side project funded from leftover resources and how it eventually became a leading technology for Google's internal use. The TPU's development and success serve as a key part of Ross's professional journey and the foundation for his current work at Groq.

Systolic Array

A systolic array is a type of computing architecture that was central to the design of the TPU. Ross explains that the TPU team chose this approach over more traditional methods, which were being used by competing teams. The systolic array was instrumental in the TPU's ability to accelerate matrix multiplication, a core operation in machine learning algorithms, and contributed to its success as an AI accelerator.

Inference

Inference in the context of AI refers to the process of applying a trained model to new, unseen data to make predictions or decisions. Ross discusses the shift in focus from training to inference as the primary use case for AI, emphasizing the need for systems optimized for inference rather than training. The video highlights the growing importance of inference in the AI market and how Groq's technology is designed to excel in this area.

Nvidia

Nvidia is a leading technology company known for its graphics processing units (GPUs), which are widely used for AI and machine learning applications. In the video, Ross compares Groq's technology with Nvidia's offerings, discussing the differences in their approaches to hardware design, software development, and market positioning. Nvidia serves as a benchmark for Groq's performance and strategic decisions.

Compiler

A compiler is a software tool that translates code written in a high-level programming language into machine-readable code. Ross mentions that Groq focused on the compiler for the first six months of their development, indicating the importance of software optimization for their hardware. The compiler's role in Groq's technology is to make programming their chips more accessible and efficient for developers.

Groq

Groq is the company founded by Jonathan Ross, which is developing AI accelerators to compete with established players like Nvidia. The video discusses Groq's origin, its innovative approach to chip design, and its strategy to differentiate itself in the market. Groq represents Ross's vision for a future where AI can be more accessible and cost-effective for businesses and developers.

Latency

Latency in the context of computing refers to the delay between the submission of a request and the reception of a response. Ross emphasizes the importance of low latency for AI applications, particularly for user-facing services like chatbots. He discusses how Groq's technology is designed to provide faster response times, which is crucial for maintaining user engagement and satisfaction.

Artificial Intelligence (AI)

Artificial Intelligence (AI) is the broad field of computer science focused on creating machines capable of intelligent behavior. Throughout the video, Ross discusses various aspects of AI, including its historical development, current applications, and future implications. AI serves as the central theme, with Ross providing insights into the technological advancements and challenges that lie ahead.

Highlights

Groq CEO Jonathan Ross discusses the rapid growth of their developer community, reaching 75,000 developers in 30 days compared to Nvidia's 7 years to reach 100,000.

Ross highlights the importance of developers in building applications and their multiplicative effect on the total number of users.

Jonathan Ross shares his unique origin story, being a high school dropout who went on to start a billion-dollar company.

Ross's journey from working at Google to founding Groq, including his work on Google's custom silicon, the TPU, which was initially a side project.

The TPU project was funded out of a VP's 'slush fund' and its success led to 'adult supervision' being brought in to manage it.

Groq's focus on compiler development for the first six months, banning whiteboards to avoid traditional chip design discussions.

The decision to build a scalable inference system, inspired by the computational demands of AlphaGo's software on TPUs.

Groq's design philosophy of needing to be 5 to 10 times better than the leading technologies to drive architectural change.

The use of older, underutilized technology like 14-nanometer processes and the avoidance of external memory to achieve performance advantages.

Groq's performance metrics of tokens per dollar, tokens per second per user, and tokens per watt, showcasing efficiency over GPUs.

Ross's comparison of Groq's performance on an 180 billion parameter model, running about 200 tokens per second, versus less than 50 on Nvidia's next-gen GPU.

The economic impact of latency on user engagement, with every 100 milliseconds increase leading to a significant drop in user interaction.

Groq's system design to handle the rapid release of new AI models, allowing for quick integration without the need for manual kernel writing.

The challenge of building a team in Silicon Valley when competing with big tech companies offering multi-million dollar packages.

Groq's strategic deals, including one with Saudi Aramco, positioning the company to surpass the compute capabilities of major hyperscalers.

Ross's perspective on the future of AI, likening large language models to telescopes for the mind, expanding our understanding of intelligence.

Groq's commitment to making AI accessible and affordable for startups, aiming to disrupt the cycle of high computational costs.