Argmax develops software that enables foundation AI models to run directly on devices, bringing advanced machine learning capabilities out of the cloud and onto local hardware. Their solutions are designed to optimize large-scale AI models—such as those used for natural language processing, vision, and other applications—so they can perform efficiently on consumer or enterprise devices without constant reliance on remote servers.
Argmax addresses the increasing demand for privacy, speed, and reliability by focusing on on-device inference. This approach helps organizations reduce latency, improve data privacy, and operate AI-powered applications even in environments with limited or intermittent connectivity. Their technology is particularly relevant for developers and product teams building device-native AI applications in healthcare, mobile, edge computing, and other sectors where real-time performance and privacy are critical.
How was Argmax started?
Argmax was founded in Palo Alto, CA in November 2023 by a team with deep expertise in machine learning infrastructure. Atila previously led ML research at Apple, building developer tools and infrastructure for on-device inference. Brian was a core architect of Apple’s inference engine during the same period. Zach joined the founding team after meeting Atila through an open-source project collaboration. Their combined experience in developing production-grade ML tools at scale informs Argmax’s focus on robust, device-side AI deployment. You can learn more about the team on their About page.
Who uses Argmax?
Argmax's technology serves organizations looking to embed advanced AI models directly onto their devices. Their customer base includes companies such as ModMed, who rely on robust, on-device machine learning to power their own products and services.
What makes Argmax different?
Argmax differentiates itself by enabling foundation models—which are traditionally run in large-scale data centers—to operate efficiently on local devices. This not only addresses privacy and latency concerns but also allows AI applications to function independently of constant cloud connectivity, which can be crucial in fields like healthcare and remote operations.
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