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Hardware & Compute3 min read

India aims to develop its own GPU by 2028 under Semiconductor Mission 2.0

India plans to create an indigenous GPU within 24 months, according to S. Krishnan, secretary of the Ministry of Electronics and Information Technology (MeitY). The goal is part of India Semiconductor Mission 2.0, which targets a broader hardware and software ecosystem for AI, including energy, accelerator chips, computing infrastructure, AI models, and applications.

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Key points

  • India targets a homegrown GPU by **November 2028** under **India Semiconductor Mission 2.0**
  • **SHAKTI** and **VEGA** processors (RISC-V-based) are nearing commercial use in robots, telescopes, and forest monitoring
  • **24 companies** received DLI funding, raising venture capital for AI-related hardware projects

The government is also advancing SHAKTI and VEGA processors, built on open-source RISC-V architecture, for commercial use. These include the VEGA ET301, a 32-bit chip running at 100MHz with 26KB SRAM, used in projects like an autonomous water-cleaning robot and a digital telescope. 24 companies received support under the Design-Linked Incentive (DLI) program, with some securing venture capital for microcontrollers, drone controllers, and smart meters. India’s semiconductor market is valued at $50 billion and aims for $100 billion by 2030, per the India Electronics and Semiconductor Association (IESA). Partnerships with ASML and Intel via Tata Group are accelerating domestic fabrication, targeting 300mm wafers and 50,000 wafers/month capacity.

Full story from Tom's Hardware · by Newton GitongaOpen source ↗

India targets creating homegrown GPUs within 2 years and plans to commercialize indigenous SHAKTI and VEGA processors

Tom's Hardware · 5 November 2026

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This text was published by Tom's Hardware and written by Newton Gitonga. It is reproduced here with attribution so you can read it in full; the rights remain with the publisher. Read it at the source ↗

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The headline, key points and digest above were generated by Digest AI's editorial model from the linked sources. Automated summaries can contain errors: the sources are the record. Spotted a mistake? Tell us. Published by Martin K., who runs Digest AI and handles corrections.

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