DEEPX's NPU chips are now being integrated into industrial systems worldwide, marking a significant milestone in the commercialization of physical AI technology. The company has secured 27 commercial orders across eight countries since beginning mass production just seven months ago, a pace that industry analysts describe as unusually swift for an emerging fabless semiconductor firm.
The adoption spans diverse application domains, including robotics, smart factories, edge AI servers, industrial AI, surveillance, AI IT services, and smart cities. These deployments are now active in Asia, North America, and Europe, demonstrating the chip's versatility and market readiness.
Global Supply Chain and Distribution
DEEPX has established a global distribution network early in its commercialization phase through partnerships with Avnet, DigiKey, and WPG. This strategy is rare for an emerging fabless company at this stage of production, providing immediate access to customers across multiple regions.
In Europe, DEEPX signed a distribution agreement with Avnet Silica, which has already identified over 30 prospective customers in high-performance embedded segments such as smart city infrastructure, autonomous mobile robots (AMR), machine vision, and smart factories. Both companies are actively expanding purchase contracts as European industrial demand for edge AI inference accelerates.
Partner Ecosystem Drives Adoption
- Renesas Electronics has integrated DEEPX NPUs into more than three types of industrial boards, combining Renesas application processors. These solutions are applicable to smart factory deployments and reflect a mature hardware ecosystem collaboration.
- Sixfab and Raspberry Pi have jointly developed an AI HAT module built around the DX-M1, alongside a real-time smart traffic analysis solution using the CES 2026 Best of Innovation-winning 'ALPON X5' AI PC that integrates DEEPX products.
- Industrial system developers such as AAEON (ASUS subsidiary), IEI, WeLink, Endrich, Toradex, and Lanner have deployed customized AI hardware solutions for smart cities, automated logistics, and security systems powered by DEEPX chips.
- Ultralytics has demonstrated 'Open-Source Physical AI Alliance' workloads running on the DEEPX NPU with a one-click deployment flow. Network Optix has validated an intelligent Video Management System capable of managing thousands of camera feeds simultaneously using DEEPX inference.
New Product Lines Simplify Integration
DEEPX has introduced two mass-production product lines designed to reduce integration friction for industrial developers
- DX-M1M (M.2 Module): A standard M.2 form factor module embedding the DX-M1 AI chip, allowing customers to add powerful AI inference capability without redesigning hardware.
- DX-AIPlayer: An edge AI acceleration solution integrating the DEEPX NPU with a high-efficiency CPU board, positioned as a 'one-stop' platform covering the full workflow from model development to industrial deployment.
Pre-Production Engagement Strategy
The company attributes its rapid adoption to a large-scale pre-production engagement strategy. Typically, it takes 9 to 18 months from proof-of-concept (PoC) initiation to full production deployment in customer applications. DEEPX began working with customers well before mass production, collaborating with approximately 350 global companies over more than one year prior to production. This approach created a large pipeline of customers already familiar with the silicon and ready to transition into commercial orders once production began.
Industry Impact and Future Outlook
The growing volume of PoC activity suggests that physical AI is moving from concept to reality, according to DEEPX CEO Lokwon Kim. With distribution in place across multiple regions and a partner ecosystem spanning silicon vendors, industrial platform makers, and open-source AI software leaders, the company appears positioned to expand its commercial footprint significantly.
If the current order velocity holds, DEEPX's ambition to establish its NPU as the default physical AI inference chip for embedded developers worldwide may be closer to reality than initially expected. This rapid adoption underscores the increasing demand for efficient, low-power AI solutions in industrial applications, reflecting broader industry trends toward edge computing and decentralized AI processing.
