Real artists ship. Extremely proud of the Lambda team for our absolute focus on delighting customers.
If you ask frontier lab and hyperscaler insiders, Lambda is absolutely beloved because of our track record of reliable and on-time delivery.
If you ask frontier lab and hyperscaler insiders, Lambda is absolutely beloved because of our track record of reliable and on-time delivery.
Others announce. We deliver.
Proud to be recognized by enterprises, hyperscalers & frontier labs.
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stephen balaban retweeted
I make this same post like every 2-3 years, because there's always new data further supporting it.
Georges Harik (@gharik) is one of the most prescient people in AI, and definitely the least well known and underappreciated one.
In 1999 he joined Google as one of the first employees (like #7 or something) because he thought he could use his AI knowledge to build a better ad system. He and Salar Kamangar built Adwords (Salar later bought YouTube and Georges later was the force behind Google wanting to buy Andy rubin's company to build Android)
Georges left Google so he could focus on AI research, which he self funded. He did so with someone he worked with closely at Google, our friend Noam Shazeer (they built the AdSense targeting engine). When Google finally decided to work hard on AI around the time of the deepmind acquisition, they tried to get Georges and Noam to come back. Georges wanted to stay independent, and our friend Noam went on to do great things obviously. This was my good fortune because it led to Georges and I hanging out a lot, because he basically needed a new lunch buddy.
Georges at the time was working on language models and despite karpathy saying nobody saw LLMs being so big even in 2017, Georges was telling me they were on the cusp of changing everything in 2013. My wife and I literally changed some of our parenting priorities that year (less tigery) because georges convinced me the world was going to change so much.
Later on he and I got involved in a company making CV photo filters called Lambda. They needed to make money to survive so they started building ML boxes which Georges bought a bunch of. But his significant other was mad there were so many loud and hot boxes in the house, she started worrying about fire risk, so Georges told the lambda guys they should build a cloud because in the future, the power density of the systems was going to be too big for even most enterprises to handle. This was like 2017. That's how lambda cloud began, and the premise Georges had was that AI was going to change things so much, demand growth would outpace supply growth, and the value of compute was eventually going to rise and that lambda was effectively going to be a REIT... so we should own as many GPUs as possible. It took the world almost a decade to finally figure out what Georges saw then (see attached tweet).
About 5 years ago when pretraining was still the only thing people cared about, Georges started telling me inference was going to be super impt. He was working on research investigating post training as a way to improve reasoning. He found some other guys working on the same idea that had a paper known as the STAR paper, and they worked together to prove it even further with another paper called quietstar. (They all later cofounded the neolab humans&). Georges got some friends to build deepinfra.com to be one of the first inference clouds because he saw it all early. He and the humans& team also smartly used all their money to buy compute knowing it would go up in price and also get harder to obtain.
It's kind of amazing to me how he has gotten everything right so far ahead of time. It's also amazing to me how VCs don't really get or believe a lot of the things he says, they often tell me I am wrong whenever I parrot Georges' current thoughts... and then years later they all put out charts and papers reaffirming those same things as if they knew it all along.
The founders of Google recognize Georges' brilliance and still talk to him a lot. (Besides being smart, he is a lot of fun to hang out with.) I'm just wondering when the rest of the world figures it out
Georges Harik (@gharik) is one of the most prescient people in AI, and definitely the least well known and underappreciated one.
In 1999 he joined Google as one of the first employees (like #7 or something) because he thought he could use his AI knowledge to build a better ad system. He and Salar Kamangar built Adwords (Salar later bought YouTube and Georges later was the force behind Google wanting to buy Andy rubin's company to build Android)
Georges left Google so he could focus on AI research, which he self funded. He did so with someone he worked with closely at Google, our friend Noam Shazeer (they built the AdSense targeting engine). When Google finally decided to work hard on AI around the time of the deepmind acquisition, they tried to get Georges and Noam to come back. Georges wanted to stay independent, and our friend Noam went on to do great things obviously. This was my good fortune because it led to Georges and I hanging out a lot, because he basically needed a new lunch buddy.
Georges at the time was working on language models and despite karpathy saying nobody saw LLMs being so big even in 2017, Georges was telling me they were on the cusp of changing everything in 2013. My wife and I literally changed some of our parenting priorities that year (less tigery) because georges convinced me the world was going to change so much.
Later on he and I got involved in a company making CV photo filters called Lambda. They needed to make money to survive so they started building ML boxes which Georges bought a bunch of. But his significant other was mad there were so many loud and hot boxes in the house, she started worrying about fire risk, so Georges told the lambda guys they should build a cloud because in the future, the power density of the systems was going to be too big for even most enterprises to handle. This was like 2017. That's how lambda cloud began, and the premise Georges had was that AI was going to change things so much, demand growth would outpace supply growth, and the value of compute was eventually going to rise and that lambda was effectively going to be a REIT... so we should own as many GPUs as possible. It took the world almost a decade to finally figure out what Georges saw then (see attached tweet).
About 5 years ago when pretraining was still the only thing people cared about, Georges started telling me inference was going to be super impt. He was working on research investigating post training as a way to improve reasoning. He found some other guys working on the same idea that had a paper known as the STAR paper, and they worked together to prove it even further with another paper called quietstar. (They all later cofounded the neolab humans&). Georges got some friends to build deepinfra.com to be one of the first inference clouds because he saw it all early. He and the humans& team also smartly used all their money to buy compute knowing it would go up in price and also get harder to obtain.
It's kind of amazing to me how he has gotten everything right so far ahead of time. It's also amazing to me how VCs don't really get or believe a lot of the things he says, they often tell me I am wrong whenever I parrot Georges' current thoughts... and then years later they all put out charts and papers reaffirming those same things as if they knew it all along.
The founders of Google recognize Georges' brilliance and still talk to him a lot. (Besides being smart, he is a lot of fun to hang out with.) I'm just wondering when the rest of the world figures it out
I can't emphasize enough how important this @a16z chart seems and how few have internalized it.
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Larson Packaging rack crate
This is the moment @oxidecomputer was built for.
Seven years ago, Eclipse backed a vision from @sdtuck and @bcantrill most thought was suicide.
Today, AI is driving unprecedented compute demand, and enterprises are rethinking what they own vs. rent.
They nailed it. en unlike anything I have ever seen.
Watching this team achieve the seemingly impossible, time and again, across engineering, supply chain, sales, finance, and beyond while scaling at an unprecedented pace has been one of the great privileges of my career.
@EclipseVentures is proud to lead Oxide's $445M Series D
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Today is Lambda's 14th birthday. Incorporated in Delaware on October 2nd, 2012. Happy Birthday Lambda!
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In 1951, Alan Turing presented this talk "Intelligent Machines, A Heretical Theory". In it he proposes a method for constructing an intelligent machine, hypothesizes about how you might interact with it, proposes something that looks similar to reinforcement learning, the seeding model communication, and superintelligence.
A great read now in the public domain.
A great read now in the public domain.
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From what I can tell, Turing was only predated by Samuel Butler on the topic of machine superintelligence as Erewhon was published in 1872.
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stephen balaban retweeted
Today, with over 100 industry partners, we introduced the NVIDIA Open Agent Safety Platform, bringing together OpenShell and Sentry.
Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to mise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility.
This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems.
Together, we are building the foundation of the AI economy.
Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. nvda.ws/4hcoq7m
Artificial intelligence is extraordinary technology that will advance discovery, productivity, security, health, and prosperity for generations to mise can only be realized when people have confidence that AI is being built to be safe and deployed with wisdom and responsibility.
This is bigger than a single product. It's the beginning of an open ecosystem to build the trust layer for safe agent systems.
Together, we are building the foundation of the AI economy.
Trust and innovation are not in conflict. Safety is how trust is earned. We must build not only the most capable AI, but the most trusted AI, so that this extraordinary technology can realize its enormous promise for the world. nvda.ws/4hcoq7m
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Correlate efficiency factors:
Power Efficiency + PUE -> MFU -> Token Efficiency -> Value Efficiency
Power Efficiency + PUE -> MFU -> Token Efficiency -> Value Efficiency
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A lot of people don't know that Lambda's first hardware product back in 2013 was actually a baseball cap with a camera embedded in the tip of the brim to gather datasets for image recognition CNNs. Cool to see this happening!
1/ Better robot models come from better data.
That takes two things: hardware that captures it fully, and tooling that turns it into training signal.
We built the first. Today, @GroundedSI launches the second: Grounded API. Access both hardware + API ↓
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The impediment to action advances action. What stands in the way becomes the way.
Whining about the impediment isn't the way. Solving it is.
Whining about the impediment isn't the way. Solving it is.
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stephen balaban retweeted
A lot of people don't really understand how important software like MaxLPS is in terms of having integration at the lowest levels in order to unlock stranded power and maximally utilize Land Power Shell (It's in the name!)
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MaxLPS maximizes the number of chips Lambda can deploy per MW. It’s amazing technology from NVIDIA and critically important for the global AI factory buildout.
More productive AI capacity from the power already available.
In a proof of concept, @LambdaAPI used NVIDIA DSX MaxLPS to run 19 nodes within the power budget of a 16-node baseline, delivering 24% more token throughput and 23% higher performance per watt.
Read the success story RXJ
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