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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 次观看 • 5 天前 •via X (Twitter)

科学技术教育

Anya Rossi• Live Now

Private livecam show

8 条评论

Miguel Ríos Berríos 的头像

Miguel Ríos Berríos5 天前

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

Sam Bessalah 的头像

Sam Bessalah5 天前

Wow. Congratulations this really good. Thanks

Behnam 的头像

Behnam5 天前

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!

Alex Xander 的头像

Alex Xander5 天前

Useful as always. Keep on the good work!

Ash 的头像

Ash5 天前

Thank you for your work!

N̷̞̬̫̆͑i̶̢͊͊̾k̸͉͉̣̀ą̵͛ 的头像

N̷̞̬̫̆͑i̶̢͊͊̾k̸͉͉̣̀ą̵͛4 天前

@luuuqqh

yeab.superint 的头像

yeab.superint4 天前

or IPUs (Inference Processing Units)

Shantanu Goel 的头像

Shantanu Goel4 天前

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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New blackboard lecture w Reiner Pope How do chips actually work – starting with basic logic gates, and working up to why GPUs, TPUs, FPGAs, and the human brain each look the way they do. 0:00:00 – Building a multiply-accumulate from logic gates 0:16:20 – Muxes and the cost of data movement 0:25:59 – How systolic arrays work 0:39:00 – Clock cycles and pipeline registers 0:51:40 – FPGAs vs ASICs 1:03:14 – Cache vs scratchpad 1:07:16 – Why CPU cores are much bigger than GPU cores 1:11:49 – Brains vs chips 1:15:22 – A GPU is just a bunch of tiny TPUs Look up Dwarkesh Podcast on YouTube/Spotify/etc to watch. Enjoy!

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Dwarkesh Patel

940,086 次观看 • 5 个月前

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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14,409 次观看 • 3 个月前

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Julia Turc

47,906 次观看 • 4 个月前

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 次观看 • 10 个月前

$GOOG TPU vs $NVDA GPU Should Google's capex guidance in Feb-26 earnings points to plateauing Nvidia dependence and accelerating TPU deployment, the boldface in Gemini 3’s model card will look like a precise timestamp on a shift in the balance of industrial power in AI.

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80,104 次观看 • 11 个月前

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Matrix Multiplication in GPU/TPU

Matrix Multiplication in GPU/TPU

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29,471 次观看 • 10 个月前

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.

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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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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.

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115,448 次观看 • 5 个月前

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490,087 次观看 • 11 个月前

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1,832,670 次观看 • 3 个月前

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138,914 次观看 • 11 个月前