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Sarvam Bets on Edge AI: Deploying Models on Feature Phones and Beyond

AI Sarvam Qualcomm Edge AI India Smart Glasses Tech Partnerships
February 18, 2026
Source: TechCrunch AI
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Localized Intelligence
Media Hype 6/10
Real Impact 8/10

Article Summary

Sarvam, backed by prominent investors, is making a bold move by deploying its AI models on unconventional devices, starting with Nokia feature phones. This strategy centers around ‘edge AI,’ which utilizes models that require only megabytes of storage and can operate offline, tapping into existing processors. The company’s demonstration at the India AI Impact Summit showcased a conversational AI assistant running on a feature phone, providing guidance in local languages. Sarvam is partnering with Qualcomm to optimize its models for Qualcomm’s chipsets and is also collaborating with Bosch for automotive AI applications and showcasing its Sarvam Kaze smart glasses. This diversified approach, coupled with a strong emphasis on sovereign AI through collaborations, signals a significant shift in Sarvam’s focus from primarily enterprise use cases to broader consumer applications. The initiative underscores the growing trend of edge computing and the potential for AI to reach previously underserved markets.

Key Points

  • Sarvam is deploying AI models on devices like Nokia feature phones, cars, and smart glasses.
  • The company’s strategy is built around ‘edge AI,’ offering offline operation and local language support.
  • Sarvam is collaborating with Qualcomm and Bosch to expand its AI ecosystem and accelerate deployment.

Why It Matters

This news is significant because it demonstrates a practical application of edge AI beyond traditional smartphones and laptops. Sarvam’s approach, targeting low-bandwidth devices like feature phones, highlights the potential of AI to democratize access to intelligent services, particularly in regions with limited internet connectivity. Furthermore, the company’s partnerships with established giants like Qualcomm and Bosch demonstrate the scalability and viability of this strategy, potentially reshaping the landscape of AI deployment.

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