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NVIDIA Solution Architect

Build your NVIDIA AI stack

Describe what you are building, or start from your organisation. Discover the technologies, architecture and requirements it needs, the gaps to close and practical next steps, including when NVIDIA technology is not needed.

Transform my business

I know AI could help us, but I’m not sure where to start.

Design my AI solution

I already know what I want to build and need the right architecture.

How it works

  1. Step 1

    Discover

    Pick your type of business from 40 sectors and over 20,000 official business descriptions. We start from documented assumptions for organisations like yours, and you confirm or correct each one.

  2. Step 2

    Prioritise

    See three to seven AI opportunities ranked by value and difficulty, with the reason for each, and an honest check on whether AI is needed at all.

  3. Step 3

    Design & plan

    Choose one opportunity to get an architecture, an NVIDIA fit assessment, the gaps to close and a phased roadmap.

Understand your architecture

Short explanations of the terms you will meet in your results, with links to NVIDIA’s own explainers.

What is inference?
Running a trained model to get an answer. In production, most of the cost of AI is inference, not training.
What is RAG?
Retrieval-augmented generation: the model first looks up your own documents, then answers using what it found, with sources.
What is an AI agent?
Software that uses a model to plan steps and use tools or systems to complete a task, ideally with people approving important actions.
What is a digital twin?
A virtual model of a real facility or process, kept in sync with data, used to test changes safely before making them.
What is physical AI?
AI that perceives and acts in the physical world, such as robots, autonomous machines and smart spaces.
What is an AI factory?
Infrastructure built to run the whole AI life cycle at scale, from data preparation to high-volume inference.
What is GPU orchestration?
Scheduling and sharing GPUs across teams and jobs, with quotas and priorities, so capacity is used instead of sitting idle.
What is computer vision?
AI that detects and understands what is in images and video, such as defects, people, vehicles or hazards.
What is edge AI?
Running AI on devices at the site (next to cameras, machines or on robots) for speed, bandwidth or privacy.
What is synthetic data?
Training data generated in simulation or by models, used when real examples are rare, costly or sensitive.
What is fine-tuning?
Further training a general model on your own examples so it handles your domain, terms or format better.
What is accelerated computing?
Using GPUs to run data processing, analytics, optimisation and AI workloads much faster than on CPUs alone.

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