GB300 容量現在開放
GB300 容量現在開放
AIFaaS™ — 人工智慧工廠即服務™
AIFaaS™ — 人工智慧工廠即服務™
AIFaaS™ — 人工智慧工廠即服務™
Inflection Point
Inflection Point
Inflection Point
企業級NVIDIA GB300基礎設施透過CambridgeNexus AIFaaS™提供。驅動下一代機構智能。
企業級NVIDIA GB300基礎設施透過CambridgeNexus AIFaaS™提供。驅動下一代機構智能。
吞吐量 + 效率
吞吐量 + 效率
推理與即時推理
推理與即時推理
準備優勢
準備優勢
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
性能基準
性能基準
從 H100 跳躍到 GB300 不是逐步的轉變，而是世代的更替。
從 H100 跳躍到 GB300 不是逐步的轉變，而是世代的更替。
業務影響指標
GPUs (H100)
GB300 NVL72 (CNEX)
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)
訓練速度 (1T 參數)
高達 4 倍更快
實時推斷吞吐量
提升30倍
能源效率 / TFLOPS
25倍效率提升
互聯帶寬
1.8 TB/s NVLink
舊版 GPU (H100)
訓練速度 (1T 參數)
基準線 (X)
實時推斷吞吐量
基準線 (X)
能源效率 / TFLOPS
標準消費
互聯帶寬
900 GB/s
為什麼選擇 CNEX？
為什麼選擇 CNEX？
我們不是雲端提供者。我們是您在布萊克威爾時代的專屬基礎設施夥伴。
我們不是雲端提供者。我們是您在布萊克威爾時代的專屬基礎設施夥伴。
客人回答更少、更聪明的问题。
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
客人回答更少、更聪明的问题。
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
預期
投資者回報率
預期
投資者回報率
預期 投資者回報率
今天就保障您的配額
今天就保障您的配額
由於重複性、高利用率的人工智能工廠收入模型驅動的高端估值潛力。
收入倍数
銷售線索增加70%
經常性的容量經濟學
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.
市場發出
準備差距的信號
市場發出
準備差距的信號
市場正在發出
準備缺口
從麥肯錫到主要雲端運營商，行業研究顯示AI的增長現在受限於基礎設施執行——電力、冷卻和部署時間。
從麥肯錫到主要雲端運營商，行業研究顯示AI的增長現在受限於基礎設施執行——電力、冷卻和部署時間。
