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OmniML is a technology company.
OmniML is an enterprise artificial intelligence company that facilitates the deployment of machine learning capabilities directly onto edge devices. Their core offering involves optimizing deep learning models to achieve enhanced speed, accuracy, and efficiency, thereby bridging the gap between sophisticated AI applications and resource-constrained hardware. This technology demonstrates significant improvements in model performance and operational cost reduction, allowing ML tasks to execute more swiftly on diverse edge platforms.
The company was established in 2021 by a team of notable experts in the AI field. Its founders include Dr. Song Han, an MIT EECS professor and serial entrepreneur, Dr. Di Wu, who previously worked as an engineer at Facebook, and Dr. Huizi Mao, recognized as a co-inventor of the “deep compression” technology developed at Stanford University. Their collective expertise and insights into optimizing AI for practical deployment led to the creation of OmniML.
OmniML engages with large enterprise customers across various vertical markets, providing solutions that enable powerful AI at the device level. The company's long-term vision is centered on the ubiquitous empowerment of edge AI, striving to make advanced artificial intelligence accessible and efficient for every edge application. This forward-looking approach aims to unlock new possibilities for intelligent systems in a decentralized computing environment.
OmniML has raised $10.0M across 1 funding round.
OmniML has raised $10.0M in total across 1 funding round.
OmniML has raised $10.0M in total across 1 funding round.
OmniML's investors include GGV Capital, Bain Capital Ventures, Hyde Park Venture Partners, Lobby Capital, Qualcomm Ventures, Foothill Ventures.
OmniML is an enterprise AI company founded in 2021 that developed software to optimize machine learning models for edge devices, enabling faster, more accurate, and efficient AI deployment on resource-constrained hardware like GPUs, FPGAs, SoCs, and MCUs.[1][3] Its core product, Omnimizer®, automates model co-design, training, and deployment, bridging the gap between powerful AI applications and edge hardware for sectors including autonomous vehicles, drones, industrial robots, video surveillance, and precision manufacturing.[1][3] Targeting enterprises in robotics, IoT, and manufacturing, OmniML solved the challenge of adapting compute-intensive AI to low-power devices, raising $10M in seed funding before its acquisition by Nvidia in February 2023, which accelerated its technology's integration into broader AI ecosystems.[1]
The company served developers, data scientists, and enterprises needing hardware-aware AI optimization, demonstrating growth through partnerships with Intel, Qualcomm, AWS AutoML, and Meta’s PyTorch, while generating leads in robotics and IoT.[1][3]
OmniML was founded in 2021 in San Jose, California, by Dr. Di Wu (CEO), Dr. Huizi Mao (CTO), and Dr. Song Han, experts in AI and machine learning whose backgrounds equipped them to tackle edge AI challenges.[1][3] The idea emerged from the mismatch between powerful AI algorithms and resource-limited edge devices in applications like autonomous vehicles, robotics, and IoT, where high computation demands hindered deployment.[3] Early traction came via $10M seed funding in March 2022 from investors including GGV Capital and Qualcomm Ventures, enabling development of Omnimizer® and partnerships—such as with Intel for AI deployment and Qualcomm for robotics demos at Hannover Messe.[1][3] These milestones validated the platform's ability to boost model performance, leading to Nvidia's acquisition in February 2023 to enhance its edge AI capabilities.[1]
OmniML rode the edge AI trend, where AI shifts from cloud to devices for low-latency, privacy-focused applications in autonomous systems, IoT, and industrial automation—driven by market forces like exploding data volumes and hardware advancements in chips from Nvidia and Qualcomm.[1][3] Its timing was ideal post-2021 AI boom, addressing the "fundamental mismatch" between AI models and edge hardware amid rising demand for efficient inference in drones, robots, and surveillance.[3] By optimizing for power efficiency, OmniML influenced the ecosystem through integrations (e.g., PyTorch, AWS) and demos, fostering faster AI deployment; its Nvidia acquisition amplifies this, enhancing AI chips and smaller models to propel edge computing's growth in manufacturing, media, and beyond.[1][2]
Post-acquisition, OmniML's technology will likely deepen Nvidia's edge AI dominance, powering next-gen chips for autonomous vehicles and robots via tools like TensorRT and Jetpack.[1][2] Trends like multimodal AI and 5G/6G will shape its trajectory, demanding even leaner models for real-time edge inferencing. Its influence may evolve from standalone optimizer to embedded Nvidia stack component, accelerating "AI everywhere" and solidifying its legacy in efficient, hardware-optimized ML.[3] This positions OmniML as a pivotal enabler in the edge AI surge, amplifying powerful machine learning to everyday devices.
OmniML has raised $10.0M across 1 funding round. Most recently, it raised $10.0M Seed in March 2022.
| Date | Round | Lead Investors | Other Investors |
|---|---|---|---|
| Mar 1, 2022 | $10.0M Seed | GGV Capital | Bain Capital Ventures, Hyde Park Venture Partners, Lobby Capital, Qualcomm Ventures, Foothill Ventures |