Conversation with Groq CEO Jonathan Ross
TLDRJonathan Ross, CEO of Groq, discusses the company's rapid growth in the AI industry, reaching 75,000 developers in just 30 days compared to Nvidia's seven years to reach 100,000. Ross shares his unique journey from being a high school dropout to a successful entrepreneur, including his pivotal role at Google where he worked on ads and contributed to the development of Google's TPU. He emphasizes the importance of developers in building applications and the multiplicative effect they have on user base expansion. Ross also highlights Groq's focus on building scalable inference systems, contrasting the company's approach with Nvidia's dominance in training. Groq aims to provide a cost-effective alternative for startups and enterprises, reducing the reliance on expensive GPU resources. The conversation touches on the future of AI, the challenges of team building in Silicon Valley, and Ross's perspective on AI's impact on jobs and society, drawing a parallel with the historical reaction to Galileo's telescope.
Takeaways
- 🚀 Groq's developer community is growing rapidly, reaching 75,000 developers in about 30 days since launching their developer console, compared to Nvidia's seven years to reach 100,000 developers.
- 🤖 Jonathan Ross, Groq's CEO, has a unique origin story, being a high school dropout who went on to work at Google and contribute to the development of Google's TPU.
- 💡 The TPU project started as a side project within Google, funded by leftover money, and aimed to solve the problem of unaffordability in deploying machine learning models at scale.
- 🔍 Ross left Google to start Groq, motivated by a desire to take a product from concept to production, which he felt he couldn't achieve within the increasingly political environment at Google.
- 🌟 Groq's approach to hardware design focuses on compiler innovation and a scalable architecture that can handle the demands of large-scale inference, setting it apart from Nvidia's more traditional and complex systems.
- ⚡ Groq's chips are designed to be significantly faster and more cost-effective than Nvidia's GPUs for inference tasks, with Groq claiming to be 5 to 10 times faster and one-tenth the cost per token.
- 📈 Nvidia excels in training and has a strong ecosystem, but Groq is positioning itself as a leader in inference, which is becoming a larger portion of the market as AI applications grow.
- 🔗 Groq's strategy is to build a scalable and efficient infrastructure for inference, which is crucial for real-time applications and enhancing user experiences.
- 💼 Ross emphasizes the economic impact of latency on user engagement and revenue, stating that reducing latency to under 300 milliseconds is key for maximizing revenue in AI applications.
- 🔑 Groq's design decisions, such as using older technology and avoiding reliance on scarce components like HBM, allow it to offer a competitive alternative to Nvidia's solutions without being constrained by the same supply chain issues.
- 🌐 The future of AI is vast, and Ross likens large language models to telescopes for the mind, suggesting that as we understand our place in the larger scope of intelligence, we will embrace rather than fear AI.
Q & A
How many developers did Groq have at the time of the conversation?
-Groq had 75,000 developers at the time of the conversation.
How long did it take Groq to reach 75,000 developers after launching their developer console?
-It took Groq about 30 days to reach 75,000 developers after launching their developer console.
Why is the number of developers important for Groq?
-Developers are important because they build applications, and each developer has a multiplicative effect on the total number of users a company can have.
What educational background does Jonathan Ross have?
-Jonathan Ross is a high school dropout who later attended Hunter College and NYU, but did not complete a degree.
How did Jonathan Ross end up at Google?
-Jonathan Ross was recognized by someone at Google who also went to NYU, leading to a referral and his subsequent employment at Google.
What was the problem that led to the development of Google's TPU?
-The problem was that machine learning models were outperforming humans but were too expensive to put into production, which would have required a significant expansion of Google's data center footprint.
What is a systolic array?
-A systolic array is a type of computing architecture that was used in the development of Google's TPU. It was initially considered outdated, but proved to be an effective solution for the TPU's needs.
Why did Jonathan Ross leave Google?
-Ross left Google due to the political nature of the company as it grew, with many people wanting to claim ownership of successful projects like TPU.
What is the main difference between training and inference in the context of AI?
-Training involves processing large amounts of data over time to teach a model, while inference is about generating predictions or responses in real-time, which requires faster performance.
Why is latency important in AI applications?
-Latency is crucial because it affects user experience and engagement. Ideally, AI applications should respond within 250-300 milliseconds to maximize revenue and user satisfaction.
How does Groq's approach differ from Nvidia's in terms of hardware and software?
-Groq focuses on a kernel-free approach and emphasizes compiler development, aiming to be more cost-effective and scalable compared to Nvidia's kernel-based, software-intensive approach.
Outlines
📈 Introduction and Developer Metrics
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 conversation focuses on Jonathan's journey, his work at Google, and the growth of developers using their platform, reaching 75,000 in 30 days compared to Nvidia's 100,000 in seven years. The importance of developers in building applications and driving user numbers is emphasized.
🚀 TPU's Inception and Market Disruption
The discussion delves into the creation of TPU (Tensor Processing Unit) at Google, which was initially a side project that grew out of '20% time' and a need to make machine learning models economically viable. Jonathan shares the innovative approach of building a matrix multiplication engine, the counterintuitive design of a systolic array, and the decision to depart from Google to pursue entrepreneurial ventures. The focus then shifts to the formation of Groq, its design philosophy, and the strategic choice to build a compiler rather than a chip.
🤖 Nvidia's Market Position and Groq's Advantages
The speaker contrasts Nvidia's strong position in the market, particularly in training and vertical integration, with Groq's focus on inference and scalable solutions. Groq's design decisions, such as using an older 14-nanometer process and avoiding reliance on scarce components like HBM (High Bandwidth Memory), are highlighted. The speaker emphasizes Groq's performance in terms of tokens per dollar, tokens per second, and tokens per watt, positioning Groq as a cost-effective alternative to Nvidia.
📉 The Economic Reality of Inference Compute
The conversation explores the economic implications of inference compute, noting the 'success disaster' where successful models lead to a significant increase in compute needs for inference, making it unaffordable at scale. The speaker discusses the limitations of Nvidia's solutions and the advantages of Groq's approach, which includes a focus on latency and user engagement, aiming to provide a superior experience at a fraction of the cost.
🔍 The Difference Between Training and Inference
The speaker clarifies the fundamental differences between training and inference in AI, emphasizing the need for speed in inference where latency is crucial for user satisfaction and engagement. The discussion also touches on the efforts made at Facebook to reduce latency and the economic incentives for providing a fast response time. The speaker asserts that a new architecture is necessary for inference, which Groq has developed, and contrasts this with Nvidia's focus on training.
🌐 Market Shift Towards Inference and Groq's Growth
The speaker predicts a significant market shift towards inference, with Groq aiming to deploy a substantial number of LPUs (presumably a type of AI accelerator developed by Groq), surpassing the capacity of major tech companies like Meta. The discussion highlights the importance of being able to quickly adapt to new models and the challenges faced by companies relying on manual kernel optimization. The speaker also addresses the rising costs associated with running large models and the potential of Groq's technology to offer a more cost-effective solution.
💼 Team Building and the Future of AI
The speaker reflects on the challenges of building a team in Silicon Valley, especially when competing with major tech companies for talent. Creative strategies for hiring and the importance of experience in shipping products are discussed. The speaker also shares insights from a deal with Saudi Aramco and the potential for Groq's customers to have more compute power than hyperscalers. The conversation concludes with the speaker's perspective on the future of AI, likening large language models to telescopes that expand our understanding of intelligence and our place within it.
Mindmap
Keywords
💡Developers
💡TPU (Tensor Processing Unit)
💡Inference
💡Compiler
💡Systolic Array
💡High School Dropout
💡AI Accelerators
💡Groq
💡Nvidia
💡Latency
💡Engagement
Highlights
Jonathan Ross, CEO of Groq, shares his unique origin story as a high school dropout who founded a billion-dollar company.
Groq has reached 75,000 developers in about 30 days since launching their developer console, a significant milestone compared to Nvidia's seven years to reach 100,000 developers.
Ross emphasizes the importance of developers in building applications and their multiplicative effect on the total number of users.
Groq's focus on a compiler-first approach aims to solve the problem of hand-optimizing models for each team at Google.
The TPU (Tensor Processing Unit) was initially developed as a side project at Google, funded out of a VP's 'slush fund' and later became a leading custom silicon used internally by Google.
Ross discusses the 'success disaster' at Google, where successful AI models were too expensive to put into production, leading to the development of the TPU.
Groq's chip architecture is designed for scaled inference, which is different from Nvidia's focus on training.
Ross predicts that the market will shift from 95% training to 90-95% inference in the next few years due to the rise of open-source models.
Groq's technology is designed to be 5-10x faster and at one-tenth the cost compared to modern GPUs for inference tasks.
The company is set to deploy 1.5 million LPUs, potentially giving them more inference AI capacity than all hyperscalers and cloud service providers combined.
Ross highlights the difficulty of team building in Silicon Valley, suggesting hiring experienced engineers who can learn AI rather than AI researchers without production experience.
Groq's partnership with Saudi Aramco and other deals positions the company to provide more compute power than a hyperscaler, offering an alternative to the locked supply chain of Nvidia.
Ross compares large language models to Galileo's telescope, suggesting that as we realize the vastness of intelligence, we will understand our place within it without fear.
Groq's design decisions, including using older technology and focusing on inference, aim to provide a 5-10x performance advantage over leading solutions.
Nvidia's vertical integration and forward integration strategies have allowed them to dominate the market, but Groq's kernel-free approach is a significant alternative.
Ross discusses the economic equation of user experience, stating that a response time under 300 milliseconds maximizes revenue.
Groq's LPUs are designed to provide high performance in both throughput and latency, outperforming Nvidia's H100s.
The future of AI, according to Ross, involves understanding our place in a vast intelligence landscape, which is both humbling and inspiring.