What is the expected growth rate for the hyperscale data center market in the area of training, both internal and cloud, over time? 
s and they use it for 3D content creation and high-definition video editing and image optimization and things like that and we introduced a brand new line of computers that we call RTX Studio. Now the OEMs were so excited about it and at SIGGRAPH, we now have 27 different laptops shipping and more coming, and so I think RTX is really geared for growth.
We have a great games coming, we got the SUPER line of GPUs. We have -- all of our notebooks that were designed into that we're ramping and of course the new RTX Studio line. And so I expect this to be a growth market for us.
C.J. Muse -- Evercore ISI -- Analyst
Very helpful. If I could follow up on the data center side, perhaps you could speak directly just to the Hyperscale side both internal and cloud and whether, you're seeing any green shoots, any signs of life there and how you're thinking about what that rate of recovery could look like over time?
Jensen Huang -- President and Chief Executive Officer
With the exception of a couple of hyperscalers, C.J., we are seeing broad based growth in data centers. In the area of training, the thing that's really exciting everybody and everybody is racing toward is training these large gigantic natural language understanding models, language models.
The Transformer model that was, that was introduced by Google called BERT has since been enhanced into XLNet and RoBERTa and, gosh, so many different -- GPT-2 and Microsoft's Mass [Phonetic] and there is so many different, different versions of these language models. And in AI, NLU natural language understanding is one of the most important areas that everybody is racing to go do, and so these models are really, really large, it's over a 1,000 times larger than image models that we were training just a few years ago and they're just gigantic models.
It's one of the reasons why we built the DGX SuperPOD so that we could train these gigantic models in a reasonable amount of time. The second -- the second area, so that's training in the hyperscalers. The second area, where we're seeing enormous amounts of activity it has to do with trying to put these conversational AI models into services so that they could be interactive and in real time, whereas photo tagging and photo enhancement is something that you could put offline and you could do that while you have excess capacity when it's off the -- off the most most busy time of the day, you can't do that with language and conversational AI, you've got to respond to the person in real time.
And so the performance that's required is significant, but more importantly, the number of models necessary for conversational AI from speech recognition to language understanding to recommendation systems to text to speech to wave synthesis these five, six, seven models have to be processed in real time in series and in real time, so that you can have a reasonable conversation with the AI agent and so these type of activities is really driving interest and activity at all of the hyperscalers.
My expectation is that this is going to continue to be a big, big growth opportunity for us. But more importantly, in addition to that, we're seeing that AI is -- the wave of AI is going from the cloud to the enterprise to the edge and all the way out to the autonomous systems. The place where we're seeing a lot of excitement and we talked about that in the past and we're seeing growth there has to do with the vertical industry enterprises that are starting to adopt AI to create new products, whether it's a delivery robot or some kind of a Chatbot or the ability to detect fraud in financial services.
These applications in vertical industries are really spreading all over the place, there is some over 4,000 AI start-ups around the world, and the way that we engage them as we -- they use our platform to start developing AI in the cloud, and as you know, we're the only AI platform that's available on-prem and in every single cloud and so they could use our AI -- AI platforms for in all the clouds, which is driving our cloud computing, external cloud computing growth.
s and they use it for 3D content creation and high-definition video editing and image optimization and things like that and we introduced a brand new line of computers that we call RTX Studio. Now the OEMs were so excited about it and at SIGGRAPH, we now have 27 different laptops shipping and more coming, and so I think RTX is really geared for growth.
We have a great games coming, we got the SUPER line of GPUs. We have -- all of our notebooks that were designed into that we're ramping and of course the new RTX Studio line. And so I expect this to be a growth market for us.
C.J. Muse -- Evercore ISI -- Analyst
Very helpful. If I could follow up on the data center side, perhaps you could speak directly just to the Hyperscale side both internal and cloud and whether, you're seeing any green shoots, any signs of life there and how you're thinking about what that rate of recovery could look like over time?
Jensen Huang -- President and Chief Executive Officer
With the exception of a couple of hyperscalers, C.J., we are seeing broad based growth in data centers. In the area of training, the thing that's really exciting everybody and everybody is racing toward is training these large gigantic natural language understanding models, language models.
The Transformer model that was, that was introduced by Google called BERT has since been enhanced into XLNet and RoBERTa and, gosh, so many different -- GPT-2 and Microsoft's Mass [Phonetic] and there is so many different, different versions of these language models. And in AI, NLU natural language understanding is one of the most important areas that everybody is racing to go do, and so these models are really, really large, it's over a 1,000 times larger than image models that we were training just a few years ago and they're just gigantic models.
 It's one of the reasons why we built the DGX SuperPOD so that we could train these gigantic models in a reasonable amount of time. The second -- the second area, so that's training in the hyperscalers. The second area, where we're seeing enormous amounts of activity it has to do with trying to put these conversational AI models into services so that they could be interactive and in real time, whereas photo tagging and photo enhancement is something that you could put offline and you could do that while you have excess capacity when it's off the -- off the most most busy time of the day, you can't do that with language and conversational AI, you've got to respond to the person in real time.
And so the performance that's required is significant, but more importantly, the number of models necessary for conversational AI from speech recognition to language understanding to recommendation systems to text to speech to wave synthesis these five, six, seven models have to be processed in real time in series and in real time, so that you can have a reasonable conversation with the AI agent and so these type of activities is really driving interest and activity at all of the hyperscalers.
My expectation is that this is going to continue to be a big, big growth opportunity for us. But more importantly, in addition to that, we're seeing that AI is -- the wave of AI is going from the cloud to the enterprise to the edge and all the way out to the autonomous systems. The place where we're seeing a lot of excitement and we talked about that in the past and we're seeing growth there has to do with the vertical industry enterprises that are starting to adopt AI to create new products, whether it's a delivery robot or some kind of a Chatbot or the ability to detect fraud in financial services.
These applications in vertical industries are really spreading all over the place, there is some over 4,000 AI start-ups around the world, and the way that we engage them as we -- they use our platform to start developing AI in the cloud, and as you know, we're the only AI platform that's available on-prem and in every single cloud and so they could use our AI -- AI platforms for in all the clouds, which is driving our cloud computing, external cloud computing growth.