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Kalyani Powertrain, Taiwan’s Compal Electronics to make servers in India

The partnership will help establish a robust server business in India, leveraging Compal’s global expertise and KPTL’s advanced manufacturing capabilities

Kalyani Powertrain, a wholly-owned subsidiary of Bharat Forge, has entered into a technology licensing agreement with Compal Electronics Inc. for manufacturing of X86 platform Servers in India.
As per the Memorandum of Understanding (MOU) signed by the two partners, Compal will provide KPTL with technological support related to servers, including overseeing local production, assembly, testing activities, and final sales.
Bharat Forge Vice Chairman & Jt MD Amit Kalyani, said: “It gives us an immense pleasure to partner with a global leader of technological products ‘Compal’ for manufacturing servers in India. This association will give a strong impetus to India’s manufacturing competitiveness. We are ecstatic and encouraged by the trust they have reposed on Kalyani group for the proposed association.”
Tony Bonadero, Chief Executive of Compal, stated, “We are very pleased to collaborate with Kalyani Powertrain. Compal is actively advancing its server business and establishing multiple partnerships. Kalyani Powertrain’s extensive experience in the Indian market will create synergistic effects in our collaboration. We look forward to this being just the beginning, with more ICT related business opportunities in the future to create greater value together.”
Additionally, the electronics division of Kalyani Powertrain announced the launch of Made in India servers from their state of art manufacturing facility at Pune, Maharashtra in February 2025. “The factory is poised to stimulate local businesses, attract and contribute significantly to the region’s manufacturing potential,” said the company in a filing with the exchanges.
The collaboration will empower enterprises, cloud providers, hyperscalers, and government organizations with high-performance, cost-efficient server solutions tailored for AI workloads, cloud computing, and large-scale data processing.

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