Knowledge Diagram Navigation:

Spatial co-ordinations are initially random, and will automatically re-arrange to minimize complexity based on distance between relationships. Mouse down and drag to pan. Right click on the strategic diagram toggles between motion and stationary. Hover over abstract node (orange) to view abstractions. Hover over leaf node to view corresponding narrative. Left click on the leaf node expands the narrative to view full text.

Narrative Analysis - Report

Key Focus

And so to be able to work closely architecturally to have our engineers work hand in hand to improve the networking performance and the computing performance has been really powerful, really terrific ...

NVIDIA accelerates everything from data processing, training, inference, every AI model, real-time speech to computer vision, and giant recommenders to vector databases. ...

And our self-driving car team, our NVIDIA research team, our generative AI team, our language model team, the amount of infrastructure that we need is quite significant. ...

The performance and versatility of our architecture translates to the lowest data center TCO and best energy efficiency. ...

No momentum supporting factor found

Challenge supporting factors

(enterprise,nvidia_ai,pre-trained)

(software,enterprise,pre-trained)

(software,models,pre-trained)

(software,nvidia_ai,enterprise,pre-trained)

(software,nvidia_ai,pre-trained)

(software,enterprise,on-prem)

(software,models,on-prem)

(software,nvidia_ai,enterprise,on-prem)

(software,nvidia_ai,on-prem)

(enterprise,gpu,nodes)

(enterprise,nvidia_ai,gpu,nodes)

(software,enterprise,nodes)

(software,gpu,nodes)

(software,nvidia_ai,enterprise,nodes)

(software,nvidia_ai,nodes)

Work-in-progress supporting factors

(nvidia,performance,tco)

(nvidia,performance,energy)

(nvidia,performance,architecturally)

(nvidia,computer,speech)

(nvidia,model,speech)

(nvidia,model,self-driving)

(nvidia,computer,recommenders)

(nvidia,model,recommenders)

(nvidia,computer,real-time)

(nvidia,model,real-time)

(nvidia,model,infrastructure)

(nvidia,computer,inference)

(nvidia,model,inference)

(nvidia,model,computer)

(nvidia,enterprise,workstations)

Time Period	Challenge	Momentum	WIP
Report	23.15	0.00	76.86

High Level Abstraction (HLA) combined

High Level Abstraction (HLA)	Report
(1) (nvidia,performance,tco)	100.00
(2) (nvidia,performance,energy)	99.75
(3) (nvidia,performance,architecturally)	99.26
(4) (nvidia,computer,speech)	98.27
(5) (nvidia,model,speech)	97.28
(6) (nvidia,model,self-driving)	96.54
(7) (nvidia,computer,recommenders)	95.80
(8) (nvidia,model,recommenders)	95.06
(9) (nvidia,computer,real-time)	94.32
(10) (nvidia,model,real-time)	93.58
(11) (nvidia,model,infrastructure)	92.59
(12) (nvidia,computer,inference)	91.36
(13) (nvidia,model,inference)	91.11
(14) (nvidia,model,computer)	89.88
(15) (nvidia,enterprise,workstations)	87.90
(16) (nvidia,workstations)	87.41
(17) (enterprise,servicenow,services)	87.16
(18) (nvidia,enterprise,services)	86.42
(19) (nvidia,enterprise,servicenow)	85.68
(20) (nvidia,enterprise,pcs)	84.94
(21) (enterprise,servicenow,lighthouse)	83.70
(22) (nvidia,enterprise,lighthouse)	83.46
(23) (nvidia,enterprise,developers)	82.96
(24) (nvidia,developers,users)	81.73
(25) (nvidia,users)	81.23
(26) (nvidia,computer,accelerates)	80.99
(27) (nvidia,vehicle)	80.00
(28) (enterprise,nvidia_ai,pre-trained)	79.01
(29) (software,enterprise,pre-trained)	77.53
(30) (software,models,pre-trained)	77.28
(31) (software,nvidia_ai,enterprise,pre-trained)	76.05
(32) (software,nvidia_ai,pre-trained)	74.81
(33) (software,dgx,pcie)	74.57
(34) (software,enterprise,pcie)	74.32
(35) (software,nvidia_ai,enterprise,pcie)	73.83
(36) (software,nvidia_ai,pcie)	71.85
(37) (software,enterprise,on-prem)	71.60
(38) (software,models,on-prem)	71.36
(39) (software,nvidia_ai,enterprise,on-prem)	71.11
(40) (software,nvidia_ai,on-prem)	68.89
(41) (enterprise,gpu,nodes)	68.64
(42) (enterprise,nvidia_ai,gpu,nodes)	65.19
(43) (software,enterprise,nodes)	61.73
(44) (software,gpu,nodes)	61.48
(45) (software,nvidia_ai,enterprise,nodes)	61.23
(46) (software,nvidia_ai,nodes)	60.49
(47) (software,enterprise,models)	60.00
(48) (software,nvidia_ai,enterprise,models)	59.51
(49) (software,nvidia_ai,models)	58.77
(50) (software,dgx,h100)	58.52
(51) (software,enterprise,h100)	58.02
(52) (software,nvidia_ai,enterprise,h100)	57.78
(53) (software,nvidia_ai,h100)	57.04
(54) (software,gpu,shortly)	56.79
(55) (software,gpu,providers)	56.54
(56) (software,gpu,nvidia_h100_tensor_core_gpus)	56.30
(57) (software,gpu,nvidia_ai)	56.05
(58) (software,gpu,enterprise)	53.33
(59) (software,gpu,dependencies)	52.84
(60) (software,services,workers)	52.35
(61) (software,services,stand-alone)	52.10
(62) (software,services,professionals)	51.60
(63) (software,services,productivity)	50.86
(64) (software,services,microsoft)	50.12
(65) (software,services,education)	49.38
(66) (software,services,ai_copilot)	48.64
(67) (software,models,velocity)	47.90
(68) (software,models,hardware)	47.65
(69) (software,models,cloud)	47.16
(70) (software,compilers)	46.67
(71) (software,dgx,compilers)	46.42
(72) (software,dgx,nvidia_ai)	46.17
(73) (software,dgx,infrastructure)	45.93
(74) (software,dgx,enterprise)	45.68
(75) (software,dgx,dollars)	45.43
(76) (software,developers)	45.19
(77) (enterprise,nvidia_ai,gpu,software)	43.70
(78) (enterprise,gpu,teraflops)	39.01
(79) (enterprise,nvidia_ai,gpu,teraflops)	37.28
(80) (enterprise,gpu,rtx)	33.33
(81) (enterprise,nvidia_ai,gpu,rtx)	31.60
(82) (enterprise,nvidia_ai,rtx)	28.15
(83) (enterprise,gpu,performance)	25.43
(84) (enterprise,nvidia_ai,gpu,performance)	23.70
(85) (enterprise,gpu,nvidia_rtx)	19.51
(86) (enterprise,nvidia_ai,gpu,nvidia_rtx)	17.78
(87) (enterprise,csps,internet,consumer)	13.83
(88) (enterprise,nvidia_ai,workstation)	13.33
(89) (enterprise,nvidia_ai,teraflops)	11.60
(90) (enterprise,nvidia_ai,performance)	10.62
(91) (enterprise,l40s,state-of-the-art)	9.14
(92) (enterprise,l40s,hyperscalers)	8.89
(93) (enterprise,servicenow,ai_lighthouse)	8.64
(94) (enterprise,internet)	8.15
(95) (enterprise,csps,consumer,revenue)	4.44
(96) (enterprise,csps,internet,revenue)	3.70
(97) (enterprise,csps,revenue)	2.72
(98) (enterprise,csps,consumer,data_center)	2.47
(99) (enterprise,csps,data_center)	1.23
(100) (enterprise,csps,internet,data_center)	0.49

Supporting narratives:

Please refer to knowledge diagram for a complete set of supporting narratives.

challenge - Back to HLA

Whether we serve customers in the cloud or on-prem through partners or direct, their applications can run seamlessly on NVIDIA AI enterprise software with access to our acceleration libraries, pre-trained models and APIs. ...
High Level Abstractions:

(enterprise,nvidia_ai,pre-trained)
(software,enterprise,models)
(software,models,pre-trained)
(software,nvidia_ai,enterprise,models)
(software,nvidia_ai,pre-trained)
(software,nvidia_ai,models)
(software,enterprise,pre-trained)
(software,nvidia_ai,enterprise,pre-trained)
Inferred entity relationships (13)
(enterprise,nvidia_ai,pre-trained,software) [inferred]
(enterprise,nvidia_ai,workstation) [inferred]
(enterprise,nvidia_ai,pcie,software) [inferred]
(nvidia_ai,pre-trained,software) [inferred]
(enterprise,nvidia_ai,rtx) [inferred]
(enterprise,nvidia_ai,pre-trained) [inferred]
(enterprise,models,software) [inferred]
(enterprise,nvidia_ai,teraflops) [inferred]
(models,nvidia_ai,software) [inferred]
(enterprise,nvidia_ai,performance) [inferred]
(enterprise,nvidia_ai,on-prem,software) [inferred]
(models,software,velocity) [inferred]
(enterprise,models,nvidia_ai,software) [inferred]

challenge - Back to HLA

Whether we serve customers in the cloud or on-prem through partners or direct, their applications can run seamlessly on NVIDIA AI enterprise software with access to our acceleration libraries, pre-trained models and APIs ...
High Level Abstractions:

(software,enterprise,on-prem)
(software,nvidia_ai,on-prem)
(software,models,on-prem)
(software,models,cloud)
(software,nvidia_ai,enterprise,on-prem)
Inferred entity relationships (9)
(enterprise,nvidia_ai,pre-trained,software) [inferred]
(enterprise,nvidia_ai,workstation) [inferred]
(enterprise,nvidia_ai,pcie,software) [inferred]
(enterprise,nvidia_ai,rtx) [inferred]
(enterprise,nvidia_ai,pre-trained) [inferred]
(nvidia_ai,on-prem,software) [inferred]
(enterprise,nvidia_ai,teraflops) [inferred]
(enterprise,nvidia_ai,performance) [inferred]
(models,software,velocity) [inferred]

challenge - Back to HLA

So from multiple instances per GPU to multiple GPUs, multiple nodes to entire data center scale. So this run time called NVIDIA AI enterprise has something like 4,500 software packages, software libraries and has something like 10,000 dependencies among each other ...
High Level Abstractions:

(software,gpu,nodes)
(enterprise,gpu,nodes)
(enterprise,nvidia_ai,gpu,software)
(software,nvidia_ai,enterprise,nodes)
(enterprise,nvidia_ai,gpu,nodes)
(software,enterprise,nodes)
(software,gpu,nvidia_ai)
(software,gpu,enterprise)
(software,nvidia_ai,nodes)
Inferred entity relationships (17)
(enterprise,gpu,nvidia_ai,software) [inferred]
(nodes,nvidia_ai,software) [inferred]
(enterprise,nodes,nvidia_ai,software) [inferred]
(enterprise,gpu,nvidia_ai,nvidia_rtx) [inferred]
(enterprise,gpu,software) [inferred]
(enterprise,gpu,teraflops) [inferred]
(enterprise,gpu,nvidia_ai,performance) [inferred]
(enterprise,gpu,nodes) [inferred]
(enterprise,gpu,rtx) [inferred]
(enterprise,gpu,nvidia_rtx) [inferred]
(enterprise,gpu,nvidia_ai,rtx) [inferred]
(enterprise,gpu,nodes,nvidia_ai) [inferred]
(enterprise,gpu,nvidia_ai,teraflops) [inferred]
(enterprise,gpu,performance) [inferred]
(gpu,nodes,software) [inferred]
(gpu,nvidia_ai,software) [inferred]
(enterprise,nodes,software) [inferred]

WIP - Back to HLA

The performance and versatility of our architecture translates to the lowest data center TCO and best energy efficiency. ...
High Level Abstractions:

(nvidia,performance,tco)
(nvidia,performance,energy)
Inferred entity relationships (1)
(nvidia,performance,tco) [inferred]

WIP - Back to HLA

And so to be able to work closely architecturally to have our engineers work hand in hand to improve the networking performance and the computing performance has been really powerful, really terrific ...
High Level Abstractions:

(nvidia,performance,architecturally)
Inferred entity relationships (1)
(nvidia,performance,tco) [inferred]

WIP - Back to HLA

NVIDIA accelerates everything from data processing, training, inference, every AI model, real-time speech to computer vision, and giant recommenders to vector databases. ...
High Level Abstractions:

(nvidia,computer,speech)
(nvidia,model,computer)
(nvidia,computer,recommenders)
(nvidia,model,recommenders)
(nvidia,model,speech)
Inferred entity relationships (7)
(computer,nvidia,speech) [inferred]
(model,nvidia,self-driving) [inferred]
(model,nvidia,speech) [inferred]
(model,nvidia,recommenders) [inferred]
(computer,nvidia,real-time) [inferred]
(model,nvidia,real-time) [inferred]
(computer,nvidia,recommenders) [inferred]

WIP - Back to HLA

And our self-driving car team, our NVIDIA research team, our generative AI team, our language model team, the amount of infrastructure that we need is quite significant ...
High Level Abstractions:

(nvidia,model,self-driving)
Inferred entity relationships (3)
(model,nvidia,recommenders) [inferred]
(model,nvidia,real-time) [inferred]
(model,nvidia,speech) [inferred]

WIP - Back to HLA

NVIDIA accelerates everything from data processing, training, inference, every AI model, real-time speech to computer vision, and giant recommenders to vector databases ...
High Level Abstractions:

(nvidia,model,real-time)
(nvidia,model,inference)
(nvidia,computer,accelerates)
(nvidia,computer,real-time)
(nvidia,computer,inference)
Inferred entity relationships (7)
(computer,nvidia,speech) [inferred]
(model,nvidia,self-driving) [inferred]
(model,nvidia,speech) [inferred]
(model,nvidia,recommenders) [inferred]
(model,nvidia,real-time) [inferred]
(computer,nvidia,real-time) [inferred]
(computer,nvidia,recommenders) [inferred]

WIP - Back to HLA

And our self-driving car team, our NVIDIA research team, our generative AI team, our language model team, the amount of infrastructure that we need is quite significant. ...
High Level Abstractions:

(nvidia,model,infrastructure)
Inferred entity relationships (4)
(model,nvidia,recommenders) [inferred]
(model,nvidia,self-driving) [inferred]
(model,nvidia,real-time) [inferred]
(model,nvidia,speech) [inferred]

WIP - Back to HLA

More developers create more applications that make NVIDIA more valuable for customers. NVIDIA is in clouds, enterprise data centers, industrial edge, PCs, workstations, instruments and robotics. ...
High Level Abstractions:

(nvidia,enterprise,workstations)
(nvidia,workstations)
(nvidia,enterprise,pcs)
Inferred entity relationships (5)
(enterprise,nvidia,pcs) [inferred]
(nvidia,workstations) [inferred]
(enterprise,nvidia,workstations) [inferred]
(enterprise,nvidia,servicenow) [inferred]
(enterprise,nvidia,services) [inferred]

WIP - Back to HLA

AI Lighthouse unites the ServiceNow enterprise automation platform and engine with NVIDIA accelerated computing and with Accenture consulting and deployment services. ...
High Level Abstractions:

(enterprise,servicenow,services)
(nvidia,enterprise,services)
Inferred entity relationships (3)
(enterprise,nvidia,pcs) [inferred]
(enterprise,nvidia,workstations) [inferred]
(enterprise,nvidia,servicenow) [inferred]

WIP - Back to HLA

AI Lighthouse unites the ServiceNow enterprise automation platform and engine with NVIDIA accelerated computing and with Accenture consulting and deployment services ...
High Level Abstractions:

(nvidia,enterprise,servicenow)
(enterprise,servicenow,lighthouse)
(nvidia,enterprise,lighthouse)
Inferred entity relationships (5)
(enterprise,nvidia,pcs) [inferred]
(enterprise,lighthouse,servicenow) [inferred]
(enterprise,nvidia,workstations) [inferred]
(enterprise,lighthouse,nvidia) [inferred]
(enterprise,nvidia,services) [inferred]

WIP - Back to HLA

More developers create more applications that make NVIDIA more valuable for customers ...
High Level Abstractions:

(nvidia,enterprise,developers)
Inferred entity relationships (4)
(enterprise,nvidia,pcs) [inferred]
(enterprise,nvidia,workstations) [inferred]
(enterprise,nvidia,servicenow) [inferred]
(enterprise,nvidia,services) [inferred]

WIP - Back to HLA

NVIDIA has hundreds of millions of CUDA-compatible GPUs worldwide. Developers need a large installed base to reach end users and grow their business. ...
High Level Abstractions:

(nvidia,developers,users)
(nvidia,users)
Inferred entity relationships (1)
(nvidia,users) [inferred]

WIP - Back to HLA

MediaTek will develop automotive SoCs and integrate a new product line of NVIDIA's GPU chiplet. The partnership covers a wide range of vehicle segments from luxury to entry level. ...
High Level Abstractions:

(nvidia,vehicle)

WIP - Back to HLA

Now we're seeing, at this point, probably hundreds of millions of dollars annually for our software business, and we are looking at NVIDIA AI enterprise to be included with many of the products that we're selling, such as our DGX, such as our PCIe versions of our H100. ...
High Level Abstractions:

(software,enterprise,pcie)
(software,dgx,pcie)
(software,nvidia_ai,enterprise,pcie)
(software,nvidia_ai,enterprise,h100)
(software,nvidia_ai,h100)
(software,nvidia_ai,pcie)
(software,dgx,h100)
(software,enterprise,h100)
Inferred entity relationships (11)
(enterprise,nvidia_ai,pre-trained,software) [inferred]
(enterprise,nvidia_ai,workstation) [inferred]
(h100,nvidia_ai,software) [inferred]
(enterprise,nvidia_ai,rtx) [inferred]
(enterprise,nvidia_ai,pre-trained) [inferred]
(enterprise,nvidia_ai,teraflops) [inferred]
(enterprise,h100,software) [inferred]
(nvidia_ai,pcie,software) [inferred]
(enterprise,nvidia_ai,performance) [inferred]
(enterprise,nvidia_ai,on-prem,software) [inferred]
(enterprise,h100,nvidia_ai,software) [inferred]

WIP - Back to HLA

Instances powered by the NVIDIA H100 Tensor Core GPUs are now generally available at AWS, Microsoft Azure and several GPU cloud providers, with others on the way shortly. ...
High Level Abstractions:

(software,gpu,shortly)
(software,gpu,providers)

WIP - Back to HLA

Instances powered by the NVIDIA H100 Tensor Core GPUs are now generally available at AWS, Microsoft Azure and several GPU cloud providers, with others on the way shortly ...
High Level Abstractions:

(software,gpu,nvidia_h100_tensor_core_gpus)

WIP - Back to HLA

So this run time called NVIDIA AI enterprise has something like 4,500 software packages, software libraries and has something like 10,000 dependencies among each other. ...
High Level Abstractions:

(software,gpu,dependencies)

WIP - Back to HLA

For example, AI Copilot such as those just announced by Microsoft can boost the productivity of over 1 billion office workers and tens of millions of software engineers. Billions of professionals in legal services, sales, customer support and education will be available to leverage AI systems trained in their field ...
High Level Abstractions:

(software,services,workers)
(software,services,professionals)
Inferred entity relationships (2)
(services,software,stand-alone) [inferred]
(services,software,workers) [inferred]

WIP - Back to HLA

And that stand-alone software continues to grow where we are providing both the software services, upgrades across there as well ...
High Level Abstractions:

(software,services,stand-alone)
Inferred entity relationships (1)
(services,software,workers) [inferred]

WIP - Back to HLA

For example, AI Copilot such as those just announced by Microsoft can boost the productivity of over 1 billion office workers and tens of millions of software engineers ...
High Level Abstractions:

(software,services,microsoft)
(software,services,productivity)
(software,services,ai_copilot)
Inferred entity relationships (2)
(services,software,stand-alone) [inferred]
(services,software,workers) [inferred]

WIP - Back to HLA

For example, AI Copilot such as those just announced by Microsoft can boost the productivity of over 1 billion office workers and tens of millions of software engineers. Billions of professionals in legal services, sales, customer support and education will be available to leverage AI systems trained in their field. ...
High Level Abstractions:

(software,services,education)
Inferred entity relationships (2)
(services,software,stand-alone) [inferred]
(services,software,workers) [inferred]

WIP - Back to HLA

And then lastly, because of our scale and velocity, we were able to sustain this really complex stack of software and hardware, networking and compute and across all of these different usage models and different computing environments ...
High Level Abstractions:

(software,models,velocity)
(software,models,hardware)
Inferred entity relationships (1)
(models,software,velocity) [inferred]

WIP - Back to HLA

And none of our optimizing compilers are possible without our DGX systems. Even compilers these days require AI, and optimizing software and infrastructure software requires AI to even develop ...
High Level Abstractions:

(software,dgx,compilers)
(software,compilers)

WIP - Back to HLA

Now we're seeing, at this point, probably hundreds of millions of dollars annually for our software business, and we are looking at NVIDIA AI enterprise to be included with many of the products that we're selling, such as our DGX, such as our PCIe versions of our H100 ...
High Level Abstractions:

(software,dgx,dollars)
(software,dgx,nvidia_ai)
(software,dgx,enterprise)

WIP - Back to HLA

And none of our optimizing compilers are possible without our DGX systems. Even compilers these days require AI, and optimizing software and infrastructure software requires AI to even develop. ...
High Level Abstractions:

(software,dgx,infrastructure)

WIP - Back to HLA

The second characteristic of our company is the installed base. You have to ask yourself, why is it that all the software developers come to our platform. And the reason for that is because software developers seek a large installed base so that they can reach the largest number of end users, so that they could build a business or get a return on the investments that they make ...
High Level Abstractions:

(software,developers)

WIP - Back to HLA

These will include powerful new RTX systems with up to 4 NVIDIA RTX 6000 GPUs, providing more than 5,800 teraflops of AI performance and 192 gigabytes of GPU memory. ...
High Level Abstractions:

(enterprise,gpu,performance)
(enterprise,nvidia_ai,gpu,performance)
(enterprise,gpu,teraflops)
(enterprise,nvidia_ai,gpu,teraflops)
Inferred entity relationships (13)
(enterprise,gpu,nvidia_rtx) [inferred]
(enterprise,gpu,nvidia_ai,rtx) [inferred]
(enterprise,gpu,nvidia_ai,software) [inferred]
(enterprise,gpu,nodes,nvidia_ai) [inferred]
(enterprise,gpu,nvidia_ai,teraflops) [inferred]
(enterprise,gpu,performance) [inferred]
(enterprise,gpu,nvidia_ai,nvidia_rtx) [inferred]
(enterprise,gpu,software) [inferred]
(gpu,nvidia_ai,software) [inferred]
(enterprise,gpu,teraflops) [inferred]
(enterprise,gpu,nodes) [inferred]
(enterprise,gpu,nvidia_ai,performance) [inferred]
(enterprise,gpu,rtx) [inferred]

WIP - Back to HLA

These will include powerful new RTX systems with up to 4 NVIDIA RTX 6000 GPUs, providing more than 5,800 teraflops of AI performance and 192 gigabytes of GPU memory ...
High Level Abstractions:

(enterprise,nvidia_ai,gpu,nvidia_rtx)
(enterprise,gpu,nvidia_rtx)
(enterprise,gpu,rtx)
(enterprise,nvidia_ai,rtx)
(enterprise,nvidia_ai,gpu,rtx)
Inferred entity relationships (20)
(enterprise,nvidia_ai,pre-trained,software) [inferred]
(enterprise,gpu,nvidia_ai,software) [inferred]
(enterprise,nvidia_ai,pcie,software) [inferred]
(enterprise,gpu,nvidia_ai,nvidia_rtx) [inferred]
(enterprise,gpu,software) [inferred]
(enterprise,gpu,teraflops) [inferred]
(enterprise,nvidia_ai,performance) [inferred]
(enterprise,gpu,nodes) [inferred]
(enterprise,gpu,nvidia_ai,performance) [inferred]
(enterprise,gpu,rtx) [inferred]
(enterprise,gpu,nvidia_rtx) [inferred]
(enterprise,nvidia_ai,workstation) [inferred]
(enterprise,gpu,nvidia_ai,rtx) [inferred]
(enterprise,gpu,nodes,nvidia_ai) [inferred]
(enterprise,gpu,nvidia_ai,teraflops) [inferred]
(enterprise,nvidia_ai,pre-trained) [inferred]
(enterprise,gpu,performance) [inferred]
(enterprise,nvidia_ai,teraflops) [inferred]
(gpu,nvidia_ai,software) [inferred]
(enterprise,nvidia_ai,on-prem,software) [inferred]

WIP - Back to HLA

They can be configured with NVIDIA AI enterprise or NVIDIA Omniverse inside.. . We also announced three new desktop workstation GPUs based on the Ada generation. ...
High Level Abstractions:

(enterprise,nvidia_ai,workstation)
Inferred entity relationships (7)
(enterprise,nvidia_ai,pre-trained,software) [inferred]
(enterprise,nvidia_ai,pcie,software) [inferred]
(enterprise,nvidia_ai,rtx) [inferred]
(enterprise,nvidia_ai,pre-trained) [inferred]
(enterprise,nvidia_ai,performance) [inferred]
(enterprise,nvidia_ai,on-prem,software) [inferred]
(enterprise,nvidia_ai,teraflops) [inferred]

WIP - Back to HLA

These will include powerful new RTX systems with up to 4 NVIDIA RTX 6000 GPUs, providing more than 5,800 teraflops of AI performance and 192 gigabytes of GPU memory. They can be configured with NVIDIA AI enterprise or NVIDIA Omniverse inside. ...
High Level Abstractions:

(enterprise,nvidia_ai,performance)
(enterprise,nvidia_ai,teraflops)
Inferred entity relationships (8)
(enterprise,nvidia_ai,pre-trained,software) [inferred]
(enterprise,nvidia_ai,workstation) [inferred]
(enterprise,nvidia_ai,pcie,software) [inferred]
(enterprise,nvidia_ai,rtx) [inferred]
(enterprise,nvidia_ai,pre-trained) [inferred]
(enterprise,nvidia_ai,performance) [inferred]
(enterprise,nvidia_ai,on-prem,software) [inferred]
(enterprise,nvidia_ai,teraflops) [inferred]

WIP - Back to HLA

And in combination with HP, Dell, and Lenovo's new server offerings based on L40S, any enterprise could have a state-of-the-art AI data center and be able to engage generative AI. ...
High Level Abstractions:

(enterprise,l40s,state-of-the-art)

WIP - Back to HLA

And so the L40S is going to -- is off to a great start and the world's enterprise and hyperscalers are really clamoring to get L40S deployed. ...
High Level Abstractions:

(enterprise,l40s,hyperscalers)

WIP - Back to HLA

We've partnered with ServiceNow and Accenture to launch the AI Lighthouse program, fast tracking the development of enterprise AI capabilities ...
High Level Abstractions:

(enterprise,servicenow,ai_lighthouse)
Inferred entity relationships (1)
(enterprise,servicenow,services) [inferred]

WIP - Back to HLA

Colette, I think last quarter, you had said CSPs were about 40% of your Data Center revenue, consumer Internet at 30%, enterprise 30%. Based on your remarks, it sounded like CSPs and consumer Internet may have been a larger percentage of your business ...
High Level Abstractions:

(enterprise,internet)

WIP - Back to HLA

Colette, I think last quarter, you had said CSPs were about 40% of your Data Center revenue, consumer Internet at 30%, enterprise 30%. ...
High Level Abstractions:

(enterprise,csps,internet,data_center)
(enterprise,csps,consumer,revenue)
(enterprise,csps,internet,revenue)
(enterprise,csps,consumer,data_center)
(enterprise,csps,revenue)
(enterprise,csps,internet,consumer)
(enterprise,csps,data_center)
Inferred entity relationships (8)
(consumer,csps,data_center,enterprise) [inferred]
(csps,data_center,enterprise) [inferred]
(csps,enterprise,revenue) [inferred]
(consumer,csps,enterprise,revenue) [inferred]
(csps,data_center,enterprise,internet) [inferred]
(csps,enterprise,internet,revenue) [inferred]
(enterprise,internet) [inferred]
(consumer,csps,enterprise,internet) [inferred]

WIP - Back to HLA

Based on your remarks, it sounded like CSPs and consumer Internet may have been a larger percentage of your business. ...
High Level Abstractions:

(enterprise,csps,internet,consumer)
Inferred entity relationships (5)
(csps,enterprise,revenue) [inferred]
(consumer,csps,enterprise,revenue) [inferred]
(csps,enterprise,internet,revenue) [inferred]
(consumer,csps,data_center,enterprise) [inferred]
(enterprise,internet) [inferred]

Semantics Relevance

NVIDIA Corp. (NVDA) Q2 2024 Earnings Call Transcript