India is targeting the development of an indigenous graphics processing unit (GPU) chip within approximately 24 months as the government steps up efforts to reduce dependence on foreign semiconductor technologies. S. Krishnan, Secretary of the Ministry of Electronics and Information Technology (MeitY), outlined the ambition in an interview published on November 4, 2026, explaining that developing domestic AI accelerator capabilities is a priority under the India Semiconductor Mission 2.0.
The proposed initiative is part of India’s broader strategy to build capabilities across the artificial intelligence technology stack, including chip design, semiconductor manufacturing, advanced packaging, computing infrastructure and AI models. The target is significant because advanced GPUs are essential for training and running modern AI systems, but the global market remains concentrated among a relatively small number of international chipmakers. However, the 24-month timeline is a development target, not confirmation that an Indian GPU is already ready for commercial production.
India Sets 24-Month Target for Indigenous GPU Development
According to Krishnan, the government intends to work towards developing an Indian GPU chip over approximately two years. The initiative will be pursued under India Semiconductor Mission 2.0, which aims to strengthen domestic capabilities across the semiconductor value chain.
The government has recognised that access to advanced computing hardware is one of the biggest challenges facing India’s AI ambitions. Most high-performance AI accelerators currently used by Indian data centres and technology companies are sourced from international suppliers.
Developing an indigenous GPU could give India greater control over an important part of its AI infrastructure. It could also create opportunities for domestic semiconductor design companies, electronics manufacturers and research institutions.
However, the government has not yet publicly detailed the proposed GPU’s architecture, manufacturing technology, performance specifications, development partners or expected production capacity. These details will be important in determining how competitive the eventual chip can become.
Why Does India Want Its Own GPU?
GPUs are specialised processors designed to perform many calculations simultaneously. This makes them particularly useful for artificial intelligence workloads, including training large language models, processing images, generating videos and running complex machine-learning applications.
Unlike conventional central processing units (CPUs), which handle a broad range of computing tasks, GPUs are designed to accelerate highly parallel workloads.
The global AI industry currently relies heavily on specialised processors from companies such as NVIDIA and AMD. NVIDIA’s GPU ecosystem, in particular, has become central to the infrastructure used by major cloud providers, AI laboratories and technology companies.
India’s dependence on imported accelerators creates several challenges:
- Supply constraints: Access to advanced GPUs can be affected by global demand, export restrictions and supply-chain disruptions.
- High infrastructure costs: Advanced accelerators are expensive, increasing the cost of building and operating AI data centres.
- Technology dependence: Domestic AI developers rely on foreign hardware platforms and associated software ecosystems.
- Strategic considerations: Sensitive government, defence and research applications may require greater control over computing infrastructure.
An indigenous GPU could help address some of these concerns over time, although developing a competitive processor and its supporting software ecosystem will require substantial investment and technical expertise.
India Semiconductor Mission 2.0 Expands Beyond Chip Assembly
The GPU initiative is connected to the government’s wider semiconductor strategy.
The first phase of the India Semiconductor Mission focused heavily on attracting semiconductor fabrication and assembly and packaging investments. The next phase, known as India Semiconductor Mission 2.0, aims to build a more complete domestic ecosystem.
The programme is expected to support several areas:
| Focus area | Government’s objective |
|---|---|
| Chip design | Develop processors and specialised semiconductor products in India |
| Semiconductor fabrication | Expand domestic wafer manufacturing capabilities |
| Advanced packaging | Build capabilities for sophisticated chip integration |
| Materials and equipment | Develop suppliers of semiconductor manufacturing inputs |
| Research and development | Support next-generation semiconductor technologies |
| AI accelerators | Develop indigenous GPU and other specialised computing chips |
The government wants India to move beyond assembling electronic products and become more involved in designing and developing the advanced components used in them.
Chip design is particularly important because it can create intellectual property and technology capabilities even before a country achieves large-scale domestic manufacturing.
India’s AI Infrastructure Strategy Has Five Layers
Krishnan explained that India’s AI strategy involves five interconnected layers rather than focusing only on developing a processor.
1. Energy and power infrastructure
AI data centres require large amounts of electricity to operate servers, cooling systems and networking equipment. Reliable and affordable power will therefore remain essential for expanding domestic AI computing capacity.
2. Semiconductor chips
The second layer involves developing indigenous accelerators and reducing dependence on imported AI processors. This is where the proposed 24-month GPU development target fits into the strategy.
3. Computing infrastructure
India also needs servers, networking equipment and storage systems to turn processors into usable computing infrastructure.
Domestic companies such as VVDN, Kaynes and Avalon have developed server manufacturing capabilities. The government is also promoting C-DAC’s Rudra server platform, including its AI-capable systems, to strengthen domestic production.
4. AI models
The IndiaAI Mission is supporting the development of indigenous AI models. The government has backed more than 20 models, covering different applications and use cases.
These include smaller, specialised models designed for sectors such as agriculture, healthcare, education and manufacturing.
5. AI applications
The final layer involves deploying AI across businesses, government services and industries. The government sees enterprise adoption as an important opportunity for Indian technology companies.
The five-layer approach shows that an indigenous GPU is one component of a much larger effort to develop domestic AI capabilities.
India Is Also Expanding Access to AI Computing Power
Alongside domestic chip development, India is working to increase access to existing GPU infrastructure.
In October 2026, Union Electronics and IT Minister Ashwini Vaishnaw announced plans for long-term procurement arrangements covering up to 10,000 AI GPUs, with assured usage over five years.
The objective is to provide greater certainty to computing infrastructure providers while making AI computing resources available to Indian startups, researchers and businesses.
Vaishnaw also discussed a separate roadmap for developing homegrown GPUs for AI inference, with solutions expected within three to four years.
This timeline is worth distinguishing from the 24-month development target mentioned by MeitY Secretary Krishnan. The government has not yet explained how the two milestones relate to each other or whether they refer to different stages of chip development and deployment.
The distinction matters because designing a processor, manufacturing it successfully, building compatible software and deploying it at commercial scale are separate challenges.
Can India Compete With NVIDIA and AMD?
Developing an indigenous GPU will be a technically demanding task. The challenge is not limited to designing the processor itself.
A competitive GPU requires a complete ecosystem that includes semiconductor architecture, memory technology, high-speed interconnects, advanced packaging, drivers, compilers and software frameworks.
NVIDIA’s competitive position, for example, is supported not only by its hardware but also by its CUDA software platform, which is widely used by AI developers.
India will therefore need to develop both hardware and software capabilities if its domestic GPU is to attract meaningful adoption.
Manufacturing is another challenge. Even if a chip is designed in India, advanced fabrication may initially depend on overseas foundries. Domestic packaging, testing and manufacturing capabilities will also determine how much of the overall supply chain can be located within the country.
The government has not announced a specific performance target or benchmark against existing commercial GPUs. It would therefore be premature to assume that the proposed processor will immediately compete with the most advanced products available internationally.
The Bigger Picture
India’s indigenous GPU ambition reflects a shift in semiconductor policy from attracting manufacturing investments towards developing strategic chip-design capabilities.
The country already has a large technology workforce, established electronics manufacturers and a growing AI market. Building domestic accelerator technology could connect these strengths and create opportunities across semiconductor design, manufacturing, data centres and AI software.
However, technological independence will not be achieved simply by producing a domestic chip. India will need reliable manufacturing partners, competitive performance, a strong developer ecosystem and commercially sustainable pricing.
Looking Ahead
The next important development will be the government’s detailed roadmap for indigenous GPU development under India Semiconductor Mission 2.0. Information about development partners, chip architecture, funding, fabrication arrangements and performance objectives will help establish how realistic the 24-month ambition is.
If India can translate the target into a working processor and eventually a commercially supported product, it could strengthen the country’s position in the global AI hardware ecosystem. For now, the announcement represents an important development goal rather than a completed semiconductor breakthrough.
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