India has six months to decide whether its next generation of engineers, doctors, and analysts learns AI in Bengaluru or misses it in Bengaluru. The NEP 2020 framework has made AI a formal classroom subject from Class 3. The AICTE “Year of AI” directive has told every engineering institution to treat AI literacy as a graduate attribute. What it all needs is hardware. Curriculum. And a partner who can install it in weeks, not semesters. That is what AI VIDYA is built for.
Why AI education cannot wait until college
The countries that will define the next decade of AI — the United States, China, Israel, Singapore — are training students on AI tools before they turn twelve. India has 260 million school-going children, 43 million students in higher education, and one of the largest ITI and polytechnic networks in the world. If even ten percent of that cohort graduates with hands-on AI exposure, India exports a workforce the global market has not yet priced. If the opposite happens, India imports AI skills from other countries for the next twenty years. The hardware inside the classroom is the lever that decides which of those two futures lands.
What “AI-ready” actually means for a classroom
Stock computer labs were built for the MS Office era. They run thin-client stacks over ten-year-old switches, one shared printer, and software licenses that expire before a generative model finishes a sentence. An AI-ready lab looks different. It needs GPU acceleration — at least one workstation-class GPU per lab, ideally more. It needs AI-capable endpoints — PCs with NPUs, not just CPUs, so students can run on-device inference for Copilot-style workflows without saturating the LAN. It needs internet bandwidth that assumes real-time model calls. And it needs a curriculum that teaches students to build with AI, not just use AI. None of that is optional anymore.
Inside the AI VIDYA lab
AI VIDYA is the programme RDP built to solve the whole stack in one engagement. A typical lab combines a GPU workstation as the classroom’s compute anchor, twenty to forty NPU-capable AI PCs as student endpoints, and a compact Mini PC serving as the local model host for inference. On top of that hardware sits an NEP 2020-aligned AI curriculum that sequences from Class 3 through Class 12 in schools, and from semester one through graduation in engineering and ITI contexts. Every lab ships with age-appropriate projects, teacher training, and an industry-recognised certification track so students leave with a credential, not just a transcript.
Three lab tiers for three institution sizes
Not every institution needs the same lab. AI VIDYA ships in three scalable configurations. The Starter tier fits a government school or a small private institution: one GPU workstation, twenty AI PCs, and the core curriculum. The Growth tier suits a college, a polytechnic, or a large CBSE school running AI across multiple grades: two GPU workstations, forty AI PCs, the full curriculum, and a dedicated model-hosting Mini PC. The Flagship tier is for engineering institutions, research-oriented universities, and state-run skilling campuses: a GPU cluster, seventy-plus AI PCs, a local LLM deployment, and integration with the institution’s existing learning management system. The bill of materials is fixed per tier — no custom BOQ, no vendor shopping, no month-long evaluation.
NEP 2020, the Class-3 AI mandate, and what changes this year
The policy runway is clear. NEP 2020 names AI as a formal subject. The 2026 academic year is the first full year when Class 3 students in NEP-aligned boards begin their AI coursework. The AICTE Year of AI directive expects every engineering student to graduate with AI competency, not a certificate course on the side. State governments are moving faster than either — several state school-education departments have already budgeted for AI lab expansion in FY 26-27, and ITI networks in at least four states have issued RFPs for AI-ready skilling infrastructure. Institutions that wait until after the academic year starts will be procuring against a demand spike that their suppliers cannot honour.
From first conversation to AI-ready in weeks
The delivery timeline is what makes AI VIDYA different from a traditional tender-based IT rollout. A typical engagement goes from site visit to functional lab in six to eight weeks. Week one is the site survey and lab design. Weeks two and three are equipment manufacturing and curriculum configuration. Weeks four and five are delivery, installation, and network setup. Weeks six and seven are teacher training and pilot classes. By week eight, students are writing their first lines of Python against a locally hosted model. The institution never touches a third-party integrator; RDP owns every layer.
Where AI VIDYA sits in the RDP portfolio
AI VIDYA is part of RDP’s Corporate & Institutional business, sitting next to our enterprise desktop and laptop programmes, but with a curriculum and certification layer no corporate line carries. The hardware inside an AI VIDYA lab draws from the same AI Computing product family that serves enterprise AI teams — GPU workstations, NPU-first AI PCs, and compact Mini PCs for edge workloads. The difference is the packaging: one bill of materials, one curriculum, one delivery promise, one institution-facing SLA. It is the only offering of its shape from an Indian OEM today.
14+ years · 100,000+ devices deployed · ISO 9001 · MeitY-recognised · PLI 2.0 selected · 8,000+ SKUs on GeM.
Take the AI VIDYA programme brief with you. Download the RDP company profile and product catalogue — a dedicated AI VIDYA brochure ships with every institution quote.
Ready to plan your AI VIDYA lab?
Whether you’re a school principal, an ITI director, a college dean, or a state IT secretary — the conversation starts the same way. Tell us about your institution. We’ll come back with a tier recommendation, a bill of materials, and a timeline.
