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I wanted to understand the structural and philosophical differences between GPUs, TPUs and newer alternatives (like Groq LPUs) without getting drowned in specs and jargon. Mispronunciation of the day: "soldered" 🤦‍♀️... 0:00 Intro 0:56 The origin story of TPUs 3:42 The first TPU: an ASIC for matrix multiplication 5:21... show more

Julia Turc

27,746 subscribers

97,852 просмотров • 5 дней назад •via X (Twitter)

Наука и технологии Образование

Anya Rossi• Live Now

Private livecam show

Комментарии: 8

Фото профиля Miguel Ríos Berríos

Miguel Ríos Berríos5 дней назад

Another banger. Thank you! Also appreciate the vid was the exact length of the morning commute.

Фото профиля Sam Bessalah

Sam Bessalah5 дней назад

Wow. Congratulations this really good. Thanks

Фото профиля Behnam

Behnam5 дней назад

Great work, the hardware engineering behind models is the next unlock that we're only scratching the surface of. Looking forward to more of these videos!

Фото профиля Alex Xander

Alex Xander5 дней назад

Useful as always. Keep on the good work!

Фото профиля Ash

Ash5 дней назад

Thank you for your work!

Фото профиля N̷̞̬̫̆͑i̶̢͊͊̾k̸͉͉̣̀ą̵͛

N̷̞̬̫̆͑i̶̢͊͊̾k̸͉͉̣̀ą̵͛4 дней назад

@luuuqqh

Фото профиля yeab.superint

yeab.superint5 дней назад

or IPUs (Inference Processing Units)

Фото профиля Shantanu Goel

Shantanu Goel4 дней назад

Really well presented! Reminded me of my time working on Google's 1st and 2nd gen Mobile TPUs, writing their firmware/kernel drivers to coordinate different workloads' required memory hitting the SRAM just at the right time. We've come a long way from there.

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Dwarkesh Patel

940,086 просмотров • 5 месяцев назад

David Patterson is a Turing Award winner famous for his contributions to computer architecture. I interviewed him about his past work, thoughts on GPU/TPUs and career advice from half a decade of experience. In this episode: • RISC vs CISC historical debate • Comparing GPUs, TPUs, and CPUs • Is Moore’s law dead? • How to have a bad career • Advice to his younger self Where to watch: • YouTube - • Spotify - • Apple Podcasts - • Transcript - Chapters: 00:00 - Intro 00:42 - RISC vs CISC 12:51 - Compilers 17:38 - GPUs 23:07 - GPU vs TPU vs CPU 32:12 - Is Moores law dead? 38:04 - GPU benchmarks 41:40 - How to have a bad career 49:59 - Courage and optimism 55:56 - Advice for his younger self 58:15 - Outro

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Ryan Peterman

14,409 просмотров • 3 месяцев назад

Early on, Jonathan Ross foresaw the unprecedented demand for compute of the 2020s. After pioneering Google's TPUs, he founded Groq, a "Language" Processing Unit dedicated for inference. After joining NVIDIA, he is betting on the GPU+LPU combo to power the agentic chains of AI calling AI calling AI. This interview is part of my larger series on dedicated AI chips, so drop any suggestions/questions below. Thank you NVIDIA for hosting us at your HQ! 00:00 Intro 01:00 The Google TPU & Groq origin story 02:41 How is Groq different from a GPU? 04:43 Static scheduling makes Groq faster 05:47 Does Groq work with Mixture-of-Experts? 09:27 Are LPUs limited to text models? 11:03 Diffusion models 13:41 NVIDIA Vera Rubin: the GPU+LPU combo 15:26 Will Groq still be sold as a standalone chip? 16:49 How does agentic AI impact inference economics? 19:21 Will AI replace CUDA kernel engineers? 21:08 Will AI democratize hardware design? 26:11 Jevon's paradox: An endless demand for compute 29:04 What should kids learn in the AI age?

Early on, Jonathan Ross foresaw the unprecedented demand for compute of the 2020s. After pioneering Google's TPUs, he founded Groq, a "Language" Processing Unit dedicated for inference. After joining NVIDIA, he is betting on the GPU+LPU combo to power the agentic chains of AI calling AI calling AI. This interview is part of my larger series on dedicated AI chips, so drop any suggestions/questions below. Thank you NVIDIA for hosting us at your HQ! 00:00 Intro 01:00 The Google TPU & Groq origin story 02:41 How is Groq different from a GPU? 04:43 Static scheduling makes Groq faster 05:47 Does Groq work with Mixture-of-Experts? 09:27 Are LPUs limited to text models? 11:03 Diffusion models 13:41 NVIDIA Vera Rubin: the GPU+LPU combo 15:26 Will Groq still be sold as a standalone chip? 16:49 How does agentic AI impact inference economics? 19:21 Will AI replace CUDA kernel engineers? 21:08 Will AI democratize hardware design? 26:11 Jevon's paradox: An endless demand for compute 29:04 What should kids learn in the AI age?

Julia Turc

47,906 просмотров • 4 месяцев назад

Good time to repost my interview with Jonathan Ross from April 2024. 0:00 - Intro 0:38 - Founding Story 3:20 - Groq Chip Memory 6:24 - Chips vs. Cloud 9:28 - Future of AI 11:04 - Where is the Value in AI? 13:45 - Agents & Inference Speed 17:18 - Optimizing Models for Groq 19:32 - Fears and Hope for the Future 22:24 - Bias and Algo Control

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Matthew Berman

26,560 просмотров • 10 месяцев назад

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JaguarAnalytics

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Sundar: “Massive interest in our GPU offerings, as well as TPUs” Anat (CFO): “TPU agreements reflected in our $462B backlog” I wrote last week: Google isn’t trying to beat NVIDIA GCP is the neutral venue where TPU and NVIDIA both monetize agents

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Ben Pouladian

28,087 просмотров • 6 месяцев назад

Matrix Multiplication in GPU/TPU

Matrix Multiplication in GPU/TPU

Mohit Mishra

29,471 просмотров • 10 месяцев назад

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Groq's LPU is faster than GPUs, handling requests and responding more quickly. Groq's LPUs don't need speedy data delivery like Nvidia GPUs do because they don't have HBM in their system. They use SRAM, which is about 20 times faster than what GPUs use. Since inference runs use way less data than model training, Groq's LPU is more energy-efficient. It reads less from external memory and uses less power than a Nvidia GPU for inference tasks. The LPU works differently from GPUs. It uses a Temporal Instruction Set Computer architecture, so it doesn't have to reload data from memory as often as GPUs do with High Bandwidth Memory (HBM). This helps avoid issues with HBM shortages and keeps costs down. If Groq's LPU is used in places that do AI processing, you might not need special storage for Nvidia GPUs. The LPU doesn't demand super-fast storage like GPUs do. Groq is claiming that its technology could replace GPUs in AI tasks with its powerful chip and software.

Brian Roemmele

87,244 просмотров • 2 лет назад

Elon Musk on the SpaceXAI satellites: “We intend for SpaceXAI satellites to allow people to use whatever GPU or TPU they want. NVIDIA GPUs, Google TPUs, Amazon Trainiums, or any other chips can be put on them. We will also offer our own chips, and we want to offer our AI software, in the future. It will be such that you can run anyone’s AI hardware or software on the SpaceXAI satellites.”

Elon Musk on the SpaceXAI satellites: “We intend for SpaceXAI satellites to allow people to use whatever GPU or TPU they want. NVIDIA GPUs, Google TPUs, Amazon Trainiums, or any other chips can be put on them. We will also offer our own chips, and we want to offer our AI software, in the future. It will be such that you can run anyone’s AI hardware or software on the SpaceXAI satellites.”

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DeFi Dad ⟠ defidad.eth

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TBPN

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Bioavailable

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This is cool, Elon Musk says SpaceX will allow their customers to use any GPU or TPU they want in SpaceX AI Satellites. "We do intend with our SpaceX AI Satellites to allow people to put whatever GPU or TPU they want. So Nvidia GPUS can be put on it, Google TPUs can be put on it, Amazon Trainium, or any other chips people want to put on. It will be such that you can run anyone’s AI, hardware or software on the SpaceX AI Satellites." An open platform design is so smart.

This is cool, Elon Musk says SpaceX will allow their customers to use any GPU or TPU they want in SpaceX AI Satellites. "We do intend with our SpaceX AI Satellites to allow people to put whatever GPU or TPU they want. So Nvidia GPUS can be put on it, Google TPUs can be put on it, Amazon Trainium, or any other chips people want to put on. It will be such that you can run anyone’s AI, hardware or software on the SpaceX AI Satellites." An open platform design is so smart.

Nic Cruz Patane

115,448 просмотров • 5 месяцев назад

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490,087 просмотров • 11 месяцев назад

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My seventh conversation with Gavin Baker. It's about the gap between what the market is doing and what companies are seeing. It's been a tough month or so for public AI names, but there's no sign of a slowdown on the ground in Silicon Valley. We discuss: - Why old GPUs are getting more expensive - Open source vs the frontier - Claude as Wall Street's Walter Cronkite - Whether the buildout gets funded out of cash flow or debt - SpaceX as a data center company - And more Enjoy! TIMESTAMPS 0:00 Intro 1:17 AI Selloff vs. Fundamentals 10:20 Financing the AI Buildout 18:18 GPU Prices Keep Rising 24:23 Claude Moves Markets 29:01 What Could Break the Thesis 36:59 The Memory Supply War 42:08 Nvidia’s New Playbook 50:14 China and Open Source 61:51 Data Centers and Regulation 71:11 SpaceX and Orbital Compute

Patrick OShaughnessy

1,832,670 просмотров • 3 месяцев назад

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Ryan Ferris

138,914 просмотров • 11 месяцев назад