Methodology and editorial standards

This is an independent educational resource published by IntelligentHQ. It is not affiliated with, sponsored by, or endorsed by NVIDIA. NVIDIA and related product names are trademarks of NVIDIA Corporation and belong to their owners. The platform uses no NVIDIA logo.

Recommendations say when NVIDIA technology is not needed, and comparisons do not name universal winners.

Product facts come from NVIDIA's own documentation and product pages. Implementations come from the company's own announcements first, then NVIDIA customer stories and reliable independent reporting. Every statement on a profile carries a numbered marker that leads to its source, with the publisher and the date the link was last checked.

Each statement has exactly one label that says who stands behind it:

Pages are not published to reach a count. The site refuses to publish a record until it passes these checks, whoever presses Publish:

• Technology profile: a verified name and type, a meaningful explanation, at least one practical use case, a category, a review date, and an official product page that answered our link check within the last 30 days.
• Full profile: in addition, capabilities each linked to a source, how it works with a diagram of its real components, limits and misconceptions, related technologies with typed relationships, and specific first steps.
• Case study: a direct source tying the exact technology to the company, the deployment status with its date, at least two sources with at least one from the company or NVIDIA, and every result attributed and marked measured or projected.
• Industry page: problems specific to that industry. Pages that only swap the industry name are rejected automatically.
• Interview: a recording appears only when the interview is published, approved, and the guest's name and role are verified.

Every cited link is checked at least once a week, slowly and one request at a time. A broken official page blocks new publication and shows on the editors' dashboard. Profiles show their last review date; a review older than 180 days is flagged for re-review. When NVIDIA renames or retires a technology, the profile uses the current name, keeps the former name for search, and a retired technology stops appearing in the labs' recommendations.

Right now the platform cites 267 distinct sources, of which 191 answered at their last check.

Every figure is arithmetic on numbers you enter. The lab assumes no prices, throughput or benchmark results.

Token economics

With C your monthly token-attributable spending, T your monthly tokens, E the cost-per-token efficiency factor and D the demand multiplier:

This is a constant unit-cost model. Spending stays flat when demand grows exactly as fast as unit cost falls. Higher demand after a price fall is possible, not inevitable. If your spending includes costs beyond inference, the results are blended costs, and the lab says so.

Infrastructure analyzer

GPU-hour cost is the number of GPUs times operating hours times the cost per GPU-hour. Electricity is IT energy times your PUE (only when you give one measured at the same boundary) times your electricity rate, and it is never added when your GPU-hour rate is a cloud rent that already includes power. The blended cost per million tokens spreads every cost you entered over all tokens. Low measured utilization is shown for context only: spare capacity is often needed for redundancy, latency targets and bursts.

The lab is a structured questionnaire, not a simulation. It scores seven foundations of a digital twin from your answers: data, 3D and model assets, integration, simulation objectives, validation, governance and operating capability. Yes counts as 100, Partly as 50 and No as 0; Not sure and unanswered questions are left out, lower the confidence of the result, and are listed as open.

The rules are applied in a fixed order. Fewer than six in ten questions answered gives no recommendation. If your first goal is to see what is happening now, if little live or operational data exists, if cameras would record public space without a privacy assessment, if no one owns the problem, or if overall readiness is below 40, the lab recommends monitoring and analytics before a twin. When the question can be answered without a twin, such as power and cooling for a few racks, the lab says a twin is not needed. Readiness of 75 or more, with validation and integration both in place, is reported as ready to scale; everything in between as a focused pilot.

NVIDIA technologies are mapped to the first use case only where it needs them, each marked core or optional with the situation in which it is not needed. Their descriptions follow the published technology profiles, which link to their sources.

The eligibility reading compares your answers with NVIDIA’s published Inception criteria, checked on 8 October 2026: the company is officially incorporated, less than 10 years old, employs at least one developer and has a working website, and it is not a consulting or outsourced development firm, crypto-related company, cloud service provider, reseller or distributor, or public company.

Any listed exclusion gives “Published requirements suggest ineligibility”. Otherwise, any unanswered criterion gives “Insufficient information”, any fixable gap, such as a missing website, gives “Requirements need attention”, and all criteria met gives “Appears to meet published basic requirements”. This is an independent, preliminary reading: NVIDIA reviews each application and decides admission and benefits, and nothing here guarantees acceptance, credits, hardware, cloud access, funding or partnership opportunities.

Technology fit and tools follow from what you are building, your stage, your main challenge and where your AI runs, using the published technology profiles. Your description is read only in your browser to suggest an area; it is never stored, shared or sent.

The labs use published, deterministic rules: the same answers always give the same result, and each recommendation shows why, when it fits, when it is unnecessary, and its source. Missing information is treated as missing, not as yes or zero. No AI service is called: nothing you type is sent to a language model.

Browsing and every lab work without an account or email. Lab answers stay in your browser. Free text you type is never stored on our server or included in a shared link. A shared result contains only the structured choices you made, and you see what it contains before you create it. Usage statistics are counted in aggregate, without identifying visitors.

How usage is counted: each page view, search, lab start or completion, export and click to an official NVIDIA site adds one to a daily total. No cookie is set, and no address, device, identifier or search text is stored. The answer to “Was this page useful?” adds to a daily count for that page; your browser remembers that you answered so the question is not repeated.

Corrections: what you send with “Suggest a correction” goes to the editors, who check it against the sources. The email address is optional, used only to reply about that correction, and erased 90 days after the correction is closed.