NVIDIA PROFESSIONAL GRAPHICS FOR Artificial Intelligence

AI Drives Real Business Success

Customer services, like Pinterest, use deep learning for image recognition, curating rich custom experiences for its users. Financial businesses like PayPal are using NVIDIA GPU-accelerated deep learning for fraud detection. Consulting firm Accenture’s R&D arm and other businesses are using deep learning to detect Internet security threats. And healthcare is one of the most important industries for the application of AI and deep learning. This sweeping change in the landscape of data-driven business is taking place across all industries. It’s changing how farmers herd their animals and forcing us to rethink transportation. The first self-driving public transit system is already up and running, giving us a glimpse into futuristic cities that will exist within our lifetimes.

Fueling All Areas of Business

AECO

Healthcare

Media & Entertainment

Government

Higher Education

Manufacturing

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Understanding AI Tokens:
The Currency of Generative AI

Every interaction with a large language model (LLM) is measured in tokens. Tokens are the individual pieces of text—words, parts of words, punctuation, and symbols—that AI models process when generating responses, analyzing documents, writing code, or answering questions.

The number of tokens processed directly impacts AI performance, response length, and operating costs. As organizations scale AI deployments across employees, applications, and workflows, token consumption can quickly grow from thousands to millions—or even billions—of tokens per month.

Why Tokens Matter

AI providers often charge based on token usage, making token volume a key factor in ongoing operational costs
Larger prompts, longer conversations, and more complex workloads require more tokens to process
Understanding token consumption helps organizations better predict AI infrastructure requirements and total cost of ownership (TCO)

The Advantage of Running AI Locally

Deploying AI models on-premises with NVIDIA-powered PNY infrastructure gives organizations greater control over how tokens are processed and managed. Rather than paying recurring per-token fees to a cloud provider, businesses can leverage dedicated AI hardware to run models locally, enabling predictable costs and greater scalability.

Benefits of Local AI Deployment

Eliminate or reduce token-based cloud usage fees
Keep sensitive data within your organization's environment
Reduce latency for faster responses and real-time inference
Maintain greater control over compliance, governance, and security
Scale AI workloads without dependency on external services

Understanding Tokens in AI

Dive into video insights showcasing what AI tokens are, why they matter, and their impact.

Token Fact

As a general guideline, 1,000 tokens is roughly equivalent to 750 words of English text. A single AI-powered application serving hundreds of users can consume millions of tokens every day, making infrastructure strategy a critical consideration for long-term AI success.

Whether you're experimenting with generative AI or deploying enterprise-scale applications, understanding token usage is essential for optimizing performance, controlling costs, and maximizing the value of your AI investment.

Compare NVIDIA RTX PRO™ Solutions

NVIDIA RTX Pro Blackwell Architecture

	NVIDIA RTX PRO 6000 Blackwell Workstation Edition	NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition	NVIDIA RTX PRO 5000 Blackwell	NVIDIA RTX PRO 4500 Blackwell	NVIDIA RTX PRO 4000 Blackwell
GPU Memory	96 GB	96 GB	48 GB	32 GB	24 GB
Memory Bandwidth	1792 GB/s	1792 GB/s	1344 GB/s	896 GB/s	673 GB/s
CUDA Cores	24,064	24,064	14,080	10,496	8,960
Tensor Cores	752	752	440	328	280
RT Cores	188	188	110	82	70
AI TOPS	4,000	3,511	2,223	1,687	1,247
Dimensions	5.4” H X 12” L, XHFL Dual Slot	4.4” H X 10.5” L, FHFL Dual Slot	4.4” H X 10.5” L, FHFL Dual Slot	4.4” H X 10.5” L, FHFL Dual Slot	4.4” H X 9.5” L, FHML Single Slot
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NVIDIA Ada Lovelace Architecture

	NVIDIA RTX 6000 Ada	NVIDIA RTX 5000 Ada	NVIDIA RTX 4500 Ada	NVIDIA RTX 4000 Ada	RTX 4000 SFF Ada	NVIDIA RTX 2000 Ada
GPU Memory	48GB GDDR6 ECC	32GB GDDR6 ECC	24GB GDDR6 ECC	20GB GDDR6 ECC	20GB GDDR6 ECC	16 GB GDDR6 with ECC
Memory Bandwidth	960 GB/s	576 GB/s	432 GB/s	360 GB/s	320 GB/s	224 GB/sec
CUDA Cores	18176	12800	7680	6144	6144	2816
Tensor Cores	568	400	240	192	192	88
RT Cores	142	100	60	48	48	22
Dimensions	4.4" x 10.5" FH \| DS	4.4" x 10.5" FH \| DS	4.4" x 10.5" FH \| DS	4.4" x 9.5" FH \| SS	4.7" x 6.6" LP \| DS	2.7” x 6.6” LP \| DS
Power & Thermal	300 W - Active	250 W - Active	210 W - Active	130 W - Active	70 W - Active	70 W - Active
	Learn More HW Sync Version	Learn More HW Sync Version	Learn More HW Sync Version	Learn More HW Sync Version	Learn More HW Sync Version	Learn More