GB300 Capacity Now Open
GB300 Capacity Now Open
The AI Infrastructure
The AI Infrastructure
The AI Infrastructure
Inflection Point
Inflection Point
Inflection Point
How GB300-Class Systems Reset the Economics of Intelligence Across Industries
How GB300-Class Systems Reset the Economics of Intelligence Across Industries
8–12× inference throughput
8–12× inference throughput
75–90% lower cost per AI action
75–90% lower cost per AI action
5–6× performance per watt
5–6× performance per watt
The Shift
The Problem vs. The Shift
The Problem vs. The Shift
Legacy GPU infrastructure behaves like a linear cost function. GB300-class systems collapse the unit economics — turning AI from selective deployment into universal deployment.
Legacy GPU infrastructure behaves like a linear cost function. GB300-class systems collapse the unit economics — turning AI from selective deployment into universal deployment.
The Problem
The Problem
Expensive inference
Expensive inference
Expensive inference
Scales linearly
Scales linearly
Scales linearly
Compressed margins
Compressed margins
Compressed margins
The Shift
The Shift
8–12×
8–12×
8–12×
inference throughput
inference throughput
75–90%
lower cost per AI action
75–90%
lower cost per AI action
75–90%
lower cost per AI action
5–6×
5–6×
5–6×
performance per watt
performance per watt
GB300 NVL72 Performance
Inference economics that reset the benchmark
Inference economics that reset the benchmark
Cost per Token
Up to 50×
Higher throughput per megawatt versus prior-gen Hopper platforms.
Throughput / MW
01
/04
Cost per Token
Up to 50×
Higher throughput per megawatt versus prior-gen Hopper platforms.
Throughput / MW
01
/04
What this means in practical terms
What this means in practical terms
Executive framing for enterprise buyers and investors.
Executive framing for enterprise buyers and investors.
Outcome
Legacy infrastructure
GB300 NVL72 impact
Low-latency inference
Higher cost per token
Higher cost per token
Up to 35× lower cost per token
Up to 35× lower cost
per token
Scaling under load
Scaling under load
Queues / contention
Queues / contention
10× higher user responsiveness
10× higher user responsiveness
Energy economics
Energy economics
Higher power overhead
Higher power overhead
Up to 50× higher throughput per MW
Up to 50× higher throughput per MW
Sustained efficiency
Sustained efficiency
Lower throughput per watt
Lower throughput per watt
5× greater throughput per watt
5× greater throughput per watt
Performance statements are expressed as “up to” and vary by model size, precision, batching, and workload characteristics.
Use for high-level positioning; final numbers should align with validated benchmarks and published references.
Performance statements are expressed as “up to” and vary by model size, precision, batching, and workload characteristics. Use for high-level positioning; final numbers should align with validated benchmarks and published references.
GB300 NVL72 (CNEX)
Low-latency inference
Up to 35× lower cost per token
Scaling under load
10× higher user responsiveness
Energy economics
Up to 50× higher throughput per MW
Sustained efficiency
5× greater throughput per watt
Legacy GPUs (H100)
Low-latency inference
Higher cost per token
Scaling under load
Queues / contention
Energy economics
Higher power overhead
Sustained efficiency
Lower throughput per watt
Sector-Wide Impact
Sector-Wide Impact
Tap a card to reveal sector impact details. (No specific company names.)
Tap a card to reveal sector impact details. (No specific company names.)
SaaS & Martech
85–90% lower inference cost • $100M–$500M revenue lift
Finance & Insurance
8–12× faster modeling • 0.5–2% AUM lift
Mobility & Delivery
70–85% lower AI cost • $500M–$1.7B efficiency gain
Retail & Luxury
Real-time personalization • Multibillion-dollar conversion lift
Biotech & MedTech
Simulations: days → hours • $500M+ pipeline acceleration
Defense
8–12× simulation speed • Strategic program acceleration
SaaS & Martech
85–90% lower inference cost • $100M–$500M revenue lift
Finance & Insurance
8–12× faster modeling • 0.5–2% AUM lift
Mobility & Delivery
70–85% lower AI cost • $500M–$1.7B efficiency gain
Retail & Luxury
Real-time personalization • Multibillion-dollar conversion lift
Biotech & MedTech
Simulations: days → hours • $500M+ pipeline acceleration
Defense
8–12× simulation speed • Strategic program acceleration
Technical
Performance Pillars
Technical
Performance Pillars
Projected Investor ROI
Four pillars that translate hardware capability into an economic moat.
Four pillars that translate hardware capability into an economic moat.
Pillar 01
Revenue Capacity
70% increase in sales leads
4–12× more revenue capacity per rack.
Pillar 01
Latency
70% increase in sales leads
50–70% lower latency for near real-time scale.
Pillar 03
Sustainability
70% increase in sales leads
5–6× better performance per watt.
Pillar 04
Infrastructure
70% increase in sales leads
Moves AI from a “feature” to a universal strategic moat.
The Market Is Signaling
a Readiness Gap
The Market Is Signaling
a Readiness Gap
The Market Is Signaling
a Readiness Gap
From McKinsey to major cloud operators, industry research shows that AI growth is now constrained by infrastructure execution — power, cooling, and time-to-deploy.
From McKinsey to major cloud operators, industry research shows that AI growth is now constrained by infrastructure execution — power, cooling, and time-to-deploy.
Infrastructure, Power & Readiness
How data centers can keep up with AI demand
Power Demand & Capital Markets
AI Is Driving a Surge in Power Demand
Rack-Scale AI & System-Level Design
NVIDIA — Rack-Scale / Data Center Systems
Independent, Investor-Respected Analysis
The AI Datacenter Is a Power Problem
Capacity Scarcity & Time-to-Deploy
CBRE — Global Data Center Outlook
Big Tech & AI Infrastructure Race
Big Tech’s AI Boom Is Running Into Power Limits
Infrastructure, Power & Readiness
How data centers can keep up with AI demand
Power Demand & Capital Markets
AI Is Driving a Surge in Power Demand
Rack-Scale AI & System-Level Design
NVIDIA — Rack-Scale / Data Center Systems
Independent, Investor-Respected Analysis
The AI Datacenter Is a Power Problem
Capacity Scarcity & Time-to-Deploy
CBRE — Global Data Center Outlook
Big Tech & AI Infrastructure Race
Big Tech’s AI Boom Is Running Into Power Limits