SqueezeBits

OwLite Meets Qualcomm Neural Network: Unlocking On-device AI Performance

Algorithms, Blog Posts, Processors, Qualcomm, Software, SqueezeBits, Tools /

This blog post was originally published at SqueezeBits’ website. It is reprinted here with the permission of SqueezeBits. At SqueezeBits we have been empowering developers to efficiently deploy complex AI models while minimizing performance trade-offs with OwLite toolkit. With OwLite v2.5, we’re excited to announce official support for Qualcomm Neural Network (QNN) through seamless integration […]

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SqueezeBits Demonstration of On-device LLM Inference, Running a 2.4B Parameter Model on the iPhone 14 Pro

Algorithms, Software, SqueezeBits, Summit 2025, Tools, Videos /

Taesu Kim, CTO of SqueezeBits, demonstrates the company’s latest edge AI and vision technologies and products at the 2025 Embedded Vision Summit. Specifically, Kim demonstrates a 2.4-billion-parameter large language model (LLM) running entirely on an iPhone 14 Pro without server connectivity. The device operates in airplane mode, highlighting on-device inference using a hybrid approach that

SqueezeBits Demonstration of On-device LLM Inference, Running a 2.4B Parameter Model on the iPhone 14 Pro Read More +

“Bridging the Gap: Streamlining the Process of Deploying AI onto Processors,” a Presentation from SqueezeBits

Algorithms, Processors, Software, SqueezeBits, Summit 2025, Tools, Videos /

Taesu Kim, Chief Technology Officer at SqueezeBits, presents the “Bridging the Gap: Streamlining the Process of Deploying AI onto Processors” tutorial at the May 2025 Embedded Vision Summit. Large language models (LLMs) often demand hand-coded conversion scripts for deployment on each distinct processor-specific software stack—a process that’s time-consuming and prone… “Bridging the Gap: Streamlining the

“Bridging the Gap: Streamlining the Process of Deploying AI onto Processors,” a Presentation from SqueezeBits Read More +

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