AI Chip Wars: LPUs, TPUs & GPUs w/ Jonathan Ross, Founder Groq
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- čas přidán 11. 06. 2024
- Who's going to win the race for AGI?
How to build AI software and hardware products?
What does it take to compete against Open AI, NVIDIA, ARM?
What is a Language Processing Unit (LPU) and AI inference speed and quality?
In this episode of Non-Consensus Investing, cut through the noise with Jonathan Ross, designer of the first TPU at Google, now building Groq to democratize AI access.
Whether you're a tech investor or an AI enthusiast, this episode is a must-listen!
He gives a comprehensive look into Groq's high-performance AI technology, detailing the roles of CPUs, GPUs, TPUs, and LPUs, and how their specially designed chips facilitate faster computations. Discussion includes forward-thinking views on the future of AI, industry shifts focusing on systems over chips, and the critical need for talent in the AI sector. Ross further advocates the role of open source in ensuring public access to AI and promoting safety, while also debunking fears of AI becoming autonomous.
00:00 Introduction and Guest Background
01:10 Demo of Groq's High Performance AI
03:35 Introduction to Groq and its Unique Positioning
05:37 Deep Dive into Groq's Chip Design and Manufacturing
07:19 Discussion on AI Hardware: CPUs, GPUs, TPUs, and LPUs
09:42 Grok's Business Model and Market Strategy
14:27 Future of AI and Groq's Role in it
35:07 Discussion on AI Philosophy and the Future of AGI
43:26 Understanding Artificial General Intelligence
43:52 Historical Perspective on AI Development
45:01 The Role of Human Intuition in AI
47:11 The Complexity of Language in AI
51:13 The Eureka Moment in AI
52:12 The Turing Test and Subjective Experience
52:35 Ethical Considerations in AGI
54:11 The Importance of Sentience in AI
56:51 The Evolution of AI in Games
56:53 The Future of AI and Civilization
59:21 The Role of Compute in AI
01:12:16 The Impact of Talent Movement in AI
01:15:48 The Future of AI: Winners and Losers
01:26:02 The Importance of Open Source in AI Safety
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Ram - please let the guest talk. We care what he has to say. He’s a major guy w major experience. Let him talk.
I think he did well. Its a great interview.
Yea I don't think ram interrupted much at all.
I agree a little. I think he was just excited and got into it even more as the interview went on but cutting off the the post information era bit annoyed me a little as I wanted to hear JR finish. I also expect that outside interviews Ram is on 4x speed so it's probably difficult to slow down!
Horrible interviewer.
@@mayurdotcano, you're brown nosing him. I wonder why.
Such a great interview. Ross has so much experience and both Ram and Ross are very knowledgeable. Learned a lot
🎯 Key Takeaways for quick navigation:
00:01 *🎤 Introduction of Jonathan Ross, CEO and founder of Groq, a high-performance AI company.*
00:28 *🧠 Jonathan's background at Google, where he developed the TPU (Tensor Processing Unit), rivaling GPUs.*
00:54 *🏁 Discussion on AI's future, AGI expectations, and Jonathan's motivations for founding Groq.*
01:35 *🚀 Demonstration of Groq's hardware running a large AI model (LLAMA 270 billion) efficiently.*
03:00 *💡 Insight into the evolving AI landscape, comparing current AI development to the late 90s' search engine wars.*
03:53 *🌐 Groq's business model: selling API services and hardware chips for AI applications.*
05:30 *🎯 Groq primarily targeting enterprise customers with latency-sensitive applications.*
05:54 *🔧 Introduction to Groq's custom-built Peta OP chip, designed for high-performance computing.*
07:00 *🤖 Explanation of the LPU's (Language Processing Unit) significance in processing sequential data efficiently, a step beyond GPUs.*
09:27 *🕒 Emphasis on latency as a critical factor in user engagement and AI application effectiveness.*
11:03 *📈 Origin story of the TPU at Google: addressing the high costs of running AI models effectively.*
13:17 *💻 Discussion on the complexities of software development in AI, contrasting Groq's approach with Nvidia's CUDA.*
14:55 *⚙️ Groq's focus on compiler development to facilitate diverse AI model compatibility without custom kernels.*
16:29 *🏗️ Insight into Groq's unique architectural approach and prioritization of software in chip design.*
18:17 *🏭 Overview of Groq's chip manufacturing process and partnership with North American companies.*
20:09 *📈 Groq's current business phase: ramping up supply chain to meet growing demand after successful demos.*
20:33 *📈 Groq is transitioning from proof of concept to building a customer pipeline and focusing on fulfilling demand.*
21:17 *💡 Groq's supply chain is orthogonal to the GPU supply chain, avoiding common limitations and adding extra capacity to the market.*
22:24 *🤔 Groq is still in the process of price discovery for their services, with a focus on their ability to produce more tokens per second than GPUs.*
23:08 *🛡️ Groq has a robust portfolio of 70 patent families, coupled with trade secrets to protect their unique technology.*
25:00 *🌐 Groq collaborates with partners for backend design but focuses on creating unique chips and software in-house.*
26:09 *💰 Groq has raised $400 million in funding, indicating significant investor confidence in their technology and business model.*
27:04 *🔑 Groq's key criteria for client engagement is existing involvement in the AI space and a commitment to using AI technology.*
29:50 *🚀 Groq targets customers who seek to transform non-latency sensitive applications into game-changing products by improving speed.*
31:11 *📊 Groq strategically enters markets where their technology offers unique advantages, avoiding direct price competition with large incumbents.*
32:07 *🤖 Groq explores potential expansion into custom chip design services, reflecting their capability and ambition to grow.*
34:10 *🛠️ Groq's innovation strategy starts with designing an optimal compiler, followed by developing the necessary hardware and IP to support it.*
36:13 *🌐 The concept of AI as a 'differently abled coworker' highlights its role as a productivity multiplier despite its limitations.*
38:45 *📚 Prompt engineering is seen as an evolving field, growing alongside advancements in AI technology and applications.*
40:34 *🚀 Rapid development and deployment of AI applications within companies signal a significant acceleration in AI-driven innovation.*
40:49 *🤔 The potential of AI is significant, but its full application is still being discovered. LLMS (large language models) are likened to a hammer searching for the right nail.*
41:27 *🤯 Discussion on the evolving definition of AGI (Artificial General Intelligence) and the shift from the Turing test to new benchmarks.*
42:10 *💡 Proposing a new test for AGI: an LLM's ability to generate a profit, such as earning a million dollars, as a measure of its practical intelligence and utility.*
43:05 *⏳ Estimation that AGI is at least five years away, with current advancements being more hype than substantial progress.*
44:13 *🧩 Historical perspective on human intelligence and AI, noting how our understanding of AI's capabilities has evolved over time through various milestones.*
45:05 *🌐 Discussion on human intuition and its representation in AI, exploring how AI models handle decision-making and probability estimation.*
47:34 *🎲 Exploration of AI's strategic decision-making, using the game of Go as an example to illustrate AI's ability to make 'intuitive' moves based on probability distributions.*
50:03 *🤖 Discussing the nature of intuition in AI, including the concept of Eureka moments where AI discovers high-probability solutions that initially seem unlikely.*
52:05 *🧠 Philosophical discussion on the possibility of AGI having subjective experiences or consciousness, and the ethical implications of such advancements.*
53:17 *🧐 Analyzing the concept of 'sensience' in AI, which involves the rate of improvement in AI's intelligence and its capability for continuous learning.*
56:13 *🌍 Discussing the necessity of civilization for continual improvement in intelligence, suggesting that collective knowledge is crucial for advancement.*
59:12 *💼 Evaluating Nvidia's competitive advantage in the AI hardware market, emphasizing their software capabilities as key to unlocking GPU performance.*
01:01:12 *🖥️ The performance of a GPU is primarily determined by HBM (High Bandwidth Memory) bandwidth rather than internal hardware differences.*
01:01:39 *💻 Nvidia's success is attributed more to its software and HBM reselling rather than hardware superiority.*
01:02:06 *🚀 Groq's achievement in AI performance with older 14-nanometer chips against Nvidia's 4-nanometer chips is credited to superior software.*
01:02:20 *🤖 Continuous development and improvement in AI software are crucial due to the constantly evolving nature of AI algorithms and applications.*
01:03:30 *⚙️ Google's challenge is to increase computational efficiency rather than merely scaling up the compute resources for AI tasks like Bard.*
01:04:24 *📈 Significant improvements in AI (like 10x improvements) are often easier to achieve than marginal ones (like 10%) due to less competition in ambitious endeavors.*
01:05:06 *🏢 Internal decision-making in large companies like Google is often more nuanced and complex than perceived externally.*
01:06:18 *💡 The importance of systems over individual chips in AI is increasing, with systems companies possibly being undervalued currently.*
01:07:56 *🌐 Google's AI project, Gemini, is seen as having potential due to founder engagement and a deep talent pool.*
01:08:37 *♟️ In the AI industry, companies strategically increase computational requirements, forcing competitors to match or surpass them.*
01:10:00 *🔄 Google's significant investment in AI research and TPU development is part of a larger, complex strategic game in the tech industry.*
01:11:22 *🤔 The AI industry is in a state of flux with talent moving between companies, indicating a dynamic and evolving field.*
01:20:19 *🚀 Idea generation in AI will become increasingly cheap, shifting focus to the delivery, access, and tailoring of AI services to end-users for value creation.*
01:20:34 *🧠 Transition from the age of information to the age of insight, emphasizing the importance of user experience and usability in AI products.*
01:21:17 *🕶️ The challenge in AI is developing intuition to effectively navigate and rank the vast amount of generated content.*
01:21:45 *🔌 Highlighting the ongoing necessity of energy consumption for AI, indicating that despite advancements in technology, power remains a fundamental requirement.*
01:22:11 *💸 Discussion on investing strategies in the AI sector, suggesting a focus on foundational technologies and components rather than betting on individual AI applications.*
01:22:26 *📈 Analyzing the semiconductor supply chain to identify profitable investment opportunities, such as companies like Corweave and Nvidia.*
01:23:08 *💻 Exploring the importance of making macro-level bets in the technology sector, particularly in the context of increasing global compute demands.*
01:24:02 *📊 Discussing Jevons' Paradox in the context of computational efficiency, predicting an ongoing increase in total compute spending as efficiency improves.*
01:25:42 *🌐 Advocating for a sector-wide investment approach in AI, focusing on companies with strong fundamentals and growth potential.*
01:26:09 *🌍 Emphasizing the importance of open-source development for AI safety and progress, countering fears of AI autonomy with the necessity of human involvement and resource availability.*
Made with HARPA AI
Good interview
He doesn't compare groq lpu with google tpu which is also specialised for matrix. Is lpu better than tpu in latency ?
Wow !!! is an under statement. I'm currently drooling like a baby.
Interesting convo. How can one invest in Groq? What was the valuation at last financing round? Thanks.
Hi, you can check out their website for more information, you can also consider joining in our waitlist if you are a qualified (accredited) investor, www.lumida.com/waitlist
Chamath was the lead investor. Watch recent mention of Groq on @allin podcast
Wondering how the LPUs are different from current architectures of H200 and MI300, which are not GPUs anymore.
Interesting, what makes you say theyre not gpu’s?
NVIDIA's dominance lasted 8 months, 22 days, 6 hours, 45 minutes and 23 seconds.
Smart guy
Meta, Amazon and Google are building their own chips. Nvidia will in fine get to sell their GPUs for training these models. Their market just got smaller suddenly.
NVDA is the short of the century.
Then do it.
If significantly more compute is required and you can maintain 75% margin then you can capitalise your successful customers and as they spend that money on your product you can use those funds to buyout the company. Therefore if Nvidia were to go on a vertical integration aquisition spree, they could do so at 61% of the cost of any other company. On that basis it's cheaper than Amazon. Food for thought...
Bad interview. Interviewer is jumping all over the place, asks leading questions, interrupts the Jonathan halfway through the answers. And worst of all the interviewer is taking every opportunity to paraphrase imputing additional things that the interviewee did not say. Jonathan is saying things in very simple language. We don't need the paraphrasing please.
Ram you are a wealth manager. He is a chip CEO. please just ask a question and then get out of the way.
Exactly. See my comment above. Was frustrating to listen. So, dropped after 20 mins.
thanks ...groq is real...grok is fake rsrs
Heartless - venture capitalist most of the time was heartless profit driven compare to engineer who provide solutions to the world problems in my opinion.
Meta just cancelled their mega order with Nvidia😅
Only criticism of this show is how often the interviewer feels the need to interject. The constant urge to show how knowledgeable you are is off putting. The “firewall” comment, pushback, and then subsequent “Yeah, of course” for example lol
You're not letting the guest speak. You need to learn to be a host before you invite more important guests.
The interviewer keeps interrupting and distracting! So annoying!
en.wikipedia.org/wiki/Jevons_paradox