On Meta's earnings call, Mark Zuckerberg said Llama 4 will need almost 10x more compute to train than Llama 3.1, Quest 3 sales exceeded expectations, and more
Meta's second quarter earnings continue the same story from the previous quarter: generative AI may be here, but it's going to take a long time to make money.
The VergeAlex Heath
Context & Ripple Effects
Meta had just positioned Llama 3.1 405B as a frontier-level open model, making the next generation’s training requirements a meaningful test of how expensive that strategy can become. The earnings commentary also frames generative AI as an adoption opportunity whose profitability remains unresolved.
Later coverage of Meta’s 100,000-plus-H100 Llama 4 training cluster shows that the stated compute step-up translated into infrastructure at exceptional scale, rather than being a purely aspirational roadmap claim.
First-order effects
Meta must commit substantially more training infrastructure to Llama 4 than it used for Llama 3.1, increasing the near-term cost and execution stakes of its model roadmap.
The company’s stronger-than-expected Quest 3 sales provide a separate positive signal for its hardware business, but do not resolve the reported lag between AI investment and AI monetization.
Second-order effects
A larger Meta training run increases demand pressure on advanced AI compute and the data-center capacity needed to operate it.
Other developers pursuing frontier or openly available models face a clearer trade-off: match escalating training scale or differentiate through smaller models, efficiency, or product distribution.
Third-order effects
If successive frontier model releases continue to require step-function increases in compute, model development is likely to concentrate among companies able to fund and secure large-scale infrastructure.
The strategic value of open-weight frontier models may increasingly depend on whether their sponsors can sustain the underlying compute economics, not only on release cadence or benchmark quality.
The trend: This is one data point in the shift from AI model releases as software events to AI model competition as an infrastructure-financing contest.
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Llama 4 is coming next year and Zuckerberg said on today's Meta earnings call it will need about 10x more compute than 3.1 that just came out. *Jensen cries tears of joy* https://www.theverge.com/...
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Llama 4 is coming next year and Zuckerberg said on today's Meta earnings call it will need about 10x more compute than 3.1 that just came out. Jensen cries tears of joy https://www.theverge.com/...
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Heard on $meta ER: “LLAMA4 will require 10X more compute than LLAMA3” Jensen invited his fastest and biggest customer. $nvda Whoever has the most cost efficient compute wins! [image]
Says aiming for Llama 4 to be the most advanced model in the industry next year. ‘The amount of compute needed to train Llama 4 will likely be almost ten times more than what we used to train Llama 3’
Mark Zuckerberg on the earnings call: 'I think we're going to look back at Llama 3.1 as an inflection point in the industry where open source AI started to become the industry standard, just like Linux is.'
Zuck said on the call the amount of compute needed to train Llama-4 will likely be almost 10x the compute needed to train Llama-3, and that future models “will continue to grow beyond that.” For context, $META used two clusters of 24K+ $NVDA H100 GPUs for Llama-3 training.
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Biggest takeaway from Zuckerberg's prepared comments is that Capex spending is going to continue. Zuckerberg says the compute for Llama 4 (out next year) is 10x more than Llama 3.
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