HomeNewsQualcomm Unveils On-Device AI Features at MWC 2024

Qualcomm Unveils On-Device AI Features at MWC 2024

At the Mobile World Congress (MWC) 2024, Qualcomm showcased an artificial intelligence (AI) features suite for smartphones. These features, powered by Snapdragon and Qualcomm platforms, operate entirely within the device, a departure from traditional models that rely on external servers.

“The future of generative AI is hybrid, with on-device intelligence working together with the cloud to provide greater personalization, privacy, reliability and efficiency,” Cristiano Amon, president and CEO of Qualcomm, said in a statement.

Highlighted offerings include a large language model (LLM) for multimodal responses and an image generation tool. Qualcomm also introduced over 75 AI models accessible to developers for specialized applications.

Unlike typical AI models, Qualcomm’s solutions are localized within the device, ensuring personalized experiences while addressing privacy concerns. These models are available to developers through platforms like Qualcomm AI Hub, GitHub, and Hugging Face.

Qualcomm emphasized the efficiency of its AI models, boasting reduced computational power consumption and cost-effectiveness for development. Despite being task-specific, these models offer diverse applications such as image editing and transcription, as seen on the Samsung Galaxy S24 Series.

“With Snapdragon 8 Gen 3 for smartphones and Snapdragon X Elite for PCs, we started commercialising on-device AI at scale. Now, with the Qualcomm AI Hub, we will empower developers to fully harness the potential of these cutting-edge technologies and create captivating AI-enabled apps,” said Durga Malladi, senior vice president and general manager of technology planning and solutions at Qualcomm Technologies.

Qualcomm integrated automation processes into its AI library to streamline development, facilitating model translation and optimizations. Additionally, it introduced research-phase tools like the Large Language and Vision Assistant (LLaVA) and Low-Rank Adaptation (LoRA) for enhanced efficiency.

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