🚀 𝗪𝗵𝗲𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗮𝗿𝗲 𝘁𝗮𝗸𝗶𝗻𝗴 𝘂𝘀 : 𝗯𝘆 𝟮𝟬𝟮𝟴, 𝗔𝗜 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗮𝗰𝗵 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗰𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗼𝗳 𝗲𝗻𝘁𝗶𝗿𝗲 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀
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🚀 𝗪𝗵𝗲𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗮𝗿𝗲 𝘁𝗮𝗸𝗶𝗻𝗴 𝘂𝘀 : 𝗯𝘆 𝟮𝟬𝟮𝟴, 𝗔𝗜 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗮𝗰𝗵 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗰𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗼𝗳 𝗲𝗻𝘁𝗶𝗿𝗲 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀
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Reminder : “Scaling laws” are empirical laws saying that  if you keep multiplying your compute by x10, your models will mechanically keep getting better and better.
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Reminder : “Scaling laws” are empirical laws saying that  if you keep multiplying your compute by x10, your models will mechanically keep getting better and better.
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To give you an idea, GPT-3 can barely write sentences, and GPT-4, which only used x15 its amount of compute, already sounds much smarter than some of my friends (although it's not really - or at least I haven't tested them side-by side). So you can imagine how far a x100 over GPT-4 can take us.
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To give you an idea, GPT-3 can barely write sentences, and GPT-4, which only used x15 its amount of compute, already sounds much smarter than some of my friends (although it's not really - or at least I haven't tested them side-by side). So you can imagine how far a x100 over GPT-4 can take us.
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🏎️ As a result, tech titans are racing to build the biggest models, and for this they need gigantic training clusters.
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🏎️ As a result, tech titans are racing to build the biggest models, and for this they need gigantic training clusters.
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The picture below shows the growth of training compute: it is increasing at a steady exponential rate of a x10 every 2 years. So let’s take this progress a bit further:
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The picture below shows the growth of training compute: it is increasing at a steady exponential rate of a x10 every 2 years. So let’s take this progress a bit further:
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- 2022: starting training for GPT-4 : 10^26 FLOPs, cost of $100M
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- 2022: starting training for GPT-4 : 10^26 FLOPs, cost of $100M
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- 2024: today, companies start training on much larger clusters like the “super AI cluster” of Elon Musk’s xAI, 10^27 FLOPS, $1B
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- 2024: today, companies start training on much larger clusters like the “super AI cluster” of Elon Musk’s xAI, 10^27 FLOPS, $1B
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- 2026 : by then clusters will require 1GW, i.e. around the full power generated by a nuclear reactor
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- 2026 : by then clusters will require 1GW, i.e. around the full power generated by a nuclear reactor
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- 2028: we reach cluster prices in the 100 billion dollars, using 10GW, more than the most powerful power stations currently in use in the US. This last size seems crazy, but Microsoft and OpenAI already are planning one.
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- 2028: we reach cluster prices in the 100 billion dollars, using 10GW, more than the most powerful power stations currently in use in the US. This last size seems crazy, but Microsoft and OpenAI already are planning one.
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Will AI clusters effectively reach these crazy sizes where the consume as much as entire countries? 
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Will AI clusters effectively reach these crazy sizes where the consume as much as entire countries? 
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➡️ Three key ingredients of training might be a roadblock to scaling up :
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➡️ Three key ingredients of training might be a roadblock to scaling up :
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💸 Money: but it’s very unlikely, given the potential market size for AGI, that investors lose interest.
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💸 Money: but it’s very unlikely, given the potential market size for AGI, that investors lose interest.
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⚡️ Energy supply at a specific location
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⚡️ Energy supply at a specific location
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📚 Training data: we’re already using 15 trillion tokens for Llama-3.1 when Internet has something like 60 trillion.
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📚 Training data: we’re already using 15 trillion tokens for Llama-3.1 when Internet has something like 60 trillion.
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🤔 I’d be curious to hear your thoughts: do you think we’ll race all the way there?
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🤔 I’d be curious to hear your thoughts: do you think we’ll race all the way there?
🚀 𝗪𝗵𝗲𝗿𝗲 𝘀𝗰𝗮𝗹𝗶𝗻𝗴 𝗹𝗮𝘄𝘀 𝗮𝗿𝗲 𝘁𝗮𝗸𝗶𝗻𝗴 𝘂𝘀 : 𝗯𝘆 𝟮𝟬𝟮𝟴, 𝗔𝗜 𝗖𝗹𝘂𝘀𝘁𝗲𝗿𝘀 𝘄𝗶𝗹𝗹 𝗿𝗲𝗮𝗰𝗵 𝘁𝗵𝗲 𝗽𝗼𝘄𝗲𝗿 𝗰𝗼𝗻𝘀𝘂𝗺𝗽𝘁𝗶𝗼𝗻 𝗼𝗳 𝗲𝗻𝘁𝗶𝗿𝗲 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀

Reminder : “Scaling laws” are empirical laws saying that  if you keep multiplying your compute by x10, your models will mechanically keep getting better and better.

To give you an idea, GPT-3 can barely write sentences, and GPT-4, which only used x15 its amount of compute, already sounds much smarter than some of my friends (although it's not really - or at least I haven't tested them side-by side). So you can imagine how far a x100 over GPT-4 can take us.

🏎️ As a result, tech titans are racing to build the biggest models, and for this they need gigantic training clusters.

The picture below shows the growth of training compute: it is increasing at a steady exponential rate of a x10 every 2 years. So let’s take this progress a bit further:
- 2022: starting training for GPT-4 : 10^26 FLOPs, cost of $100M
- 2024: today, companies start training on much larger clusters like the “super AI cluster” of Elon Musk’s xAI, 10^27 FLOPS, $1B
- 2026 : by then clusters will require 1GW, i.e. around the full power generated by a nuclear reactor
- 2028: we reach cluster prices in the 100 billion dollars, using 10GW, more than the most powerful power stations currently in use in the US. This last size seems crazy, but Microsoft and OpenAI already are planning one.

Will AI clusters effectively reach these crazy sizes where the consume as much as entire countries? 
➡️ Three key ingredients of training might be a roadblock to scaling up :
💸 Money: but it’s very unlikely, given the potential market size for AGI, that investors lose interest.
⚡️ Energy supply at a specific location
📚 Training data: we’re already using 15 trillion tokens for Llama-3.1 when Internet has something like 60 trillion.

🤔 I’d be curious to hear your thoughts: do you think we’ll race all the way there?
👀
John6666 Kaoeiri
👍
lamhieu Kaoeiri
600ae38cc92b79f54efd4556
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Hieu Lam
lamhieu
user
65921e3dcdc0c4099b83daa3
/avatars/ac25f29292cca71ab6d509ea781e7943.svg
Shareef Taylor
MANOFAi94
user
662162fd296b3d40f15367a4
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Stephen Genusa
StephenGenusa
user