Eiso Kant

When we're scaling up the size of these models and we're training them on more and more data and we're using more and more compute for it, at every single step that we're taking in the learning, every set of samples of data that we show the model, we need them to communicate with each other and share what they've learned. across the optimization landscape.

When we're scaling up the size of these models and we're training them on more and more data and we're using more and more compute for it, at every single step that we're taking in the learning, every set of samples of data that we show the model, we need them to communicate with each other and share what they've learned. across the optimization landscape.

And so this means that if I would, you know, have two data centers that sit far away from each other, the amount of information that they have to share with each other, all of the different servers, and we're talking here thousands and tens of thousands soon of servers, you know, would make it so slow that it wouldn't be economically viable to train these models.

And so this means that if I would, you know, have two data centers that sit far away from each other, the amount of information that they have to share with each other, all of the different servers, and we're talking here thousands and tens of thousands soon of servers, you know, would make it so slow that it wouldn't be economically viable to train these models.

And so this means that if I would, you know, have two data centers that sit far away from each other, the amount of information that they have to share with each other, all of the different servers, and we're talking here thousands and tens of thousands soon of servers, you know, would make it so slow that it wouldn't be economically viable to train these models.

Once I'm running a model, I'm using a lot less servers to run it. So think of it as having lots and lots of copies of the model during training over lots and lots of machines that every time they see data to learn, they need to communicate with each other to continue to improve in their learning.

Once I'm running a model, I'm using a lot less servers to run it. So think of it as having lots and lots of copies of the model during training over lots and lots of machines that every time they see data to learn, they need to communicate with each other to continue to improve in their learning.

Once I'm running a model, I'm using a lot less servers to run it. So think of it as having lots and lots of copies of the model during training over lots and lots of machines that every time they see data to learn, they need to communicate with each other to continue to improve in their learning.

The dynamic that exists in the world today, we all owe a depth of gratitude to NVIDIA. When I started in this space in 2016, we were stacking 1080 Ti chips in racks of servers at our office. And NVIDIA already back then understood that AI was going to be world-changing. And no other company, the exception of maybe Google, had that deep of a realization.

The dynamic that exists in the world today, we all owe a depth of gratitude to NVIDIA. When I started in this space in 2016, we were stacking 1080 Ti chips in racks of servers at our office. And NVIDIA already back then understood that AI was going to be world-changing. And no other company, the exception of maybe Google, had that deep of a realization.

The dynamic that exists in the world today, we all owe a depth of gratitude to NVIDIA. When I started in this space in 2016, we were stacking 1080 Ti chips in racks of servers at our office. And NVIDIA already back then understood that AI was going to be world-changing. And no other company, the exception of maybe Google, had that deep of a realization.

But Nvidia had massive conviction on this and continued to double down here and has made more and more incredible hardware. The company that fast followed on that was Google. That's why we're on the fifth generation of TPUs. And the company that fast followed on that was Amazon.

But Nvidia had massive conviction on this and continued to double down here and has made more and more incredible hardware. The company that fast followed on that was Google. That's why we're on the fifth generation of TPUs. And the company that fast followed on that was Amazon.

But Nvidia had massive conviction on this and continued to double down here and has made more and more incredible hardware. The company that fast followed on that was Google. That's why we're on the fifth generation of TPUs. And the company that fast followed on that was Amazon.

The reason I mentioned those three specifically is that they are all building extremely large volume of chips and constantly iterating on faster and better and better generations of chips for training and for inference. They're for me the three primary players in the race, just from sheer volume of what they're producing in the fabs versus what they're bringing online to end users.

The reason I mentioned those three specifically is that they are all building extremely large volume of chips and constantly iterating on faster and better and better generations of chips for training and for inference. They're for me the three primary players in the race, just from sheer volume of what they're producing in the fabs versus what they're bringing online to end users.

The reason I mentioned those three specifically is that they are all building extremely large volume of chips and constantly iterating on faster and better and better generations of chips for training and for inference. They're for me the three primary players in the race, just from sheer volume of what they're producing in the fabs versus what they're bringing online to end users.

Now, we have some other companies in this space, AMD, competitor to Nvidia, doesn't have their own cloud, is reliant on being in this competitive nature from a price perspective with Nvidia. And so their ramp up is entirely determined by the demand of the world wanting to use their chips.

Now, we have some other companies in this space, AMD, competitor to Nvidia, doesn't have their own cloud, is reliant on being in this competitive nature from a price perspective with Nvidia. And so their ramp up is entirely determined by the demand of the world wanting to use their chips.

Now, we have some other companies in this space, AMD, competitor to Nvidia, doesn't have their own cloud, is reliant on being in this competitive nature from a price perspective with Nvidia. And so their ramp up is entirely determined by the demand of the world wanting to use their chips.