Dublin, March 05, 2026 (GLOBE NEWSWIRE) -- The "Automotive AI Box Research Report, 2026" report has been added to ResearchAndMarkets.com's offering.
Automotive AI Box Research: A new path of edge AI accelerates
This report studies the current application status of automotive AI Box from the aspects of scenario demand, product configuration, and industry chain collaboration, and explores the future trends of automotive AI Box.
AI Box is the "accelerator" for the implementation of edge AI
The "edge-cloud collaboration" solution has become a consensus for the implementation of automotive AI, that is, edge AI solves high-frequency, real-time, privacy-sensitive tasks (such as local data processing, real-time perception, and rapid response), and cloud AI is responsible for complex reasoning, model optimization, and large-scale data storage analysis. The edge/cloud AI division of labor is clear, which reduces the difficulty of deployment and improves AI operating efficiency.
Compared with cloud AI, edge AI has natural advantages in real-time performance and privacy protection. However, as the iteration of AI functions accelerates, typical new problems of edge AI have emerged:
The computing power of the old vehicle model cannot support new AI functions: With the addition of complex functions such as AI Agent, the fixed computing power of the original vehicle integrated chip is often unable to support the continuously growing algorithm demand.
The performance of the existing model cannot cope with the continuous flow of new scenarios: the complexity and number of AI application scenarios have increased. The original vehicle's edge AI model has limited performance after pruning and quantification, and cannot make accurate reasoning and predictions for newly added complex scenarios.
The automotive AI Box can solve the above two problems: on the one hand, it uses a large computing power chip to increase the upper limit of the original vehicle's computing power, providing sufficient computing power support for the implementation of new algorithms and new functions; on the other hand, it presets a basic AI algorithm framework, which not only retains the real-time nature of edge reasoning, but also supports the delivery of optimized lightweight model update packages through the cloud, achieving the continuous evolution of edge AI capabilities, and then relying on its own large computing power to improve AI reasoning/decision-making capabilities under complex scenarios.
Taking supplemental computing power as an example, current edge AI models generally have 1-8 billion parameters, and the computing power requirements of foundation models with a varying number of parameters show clear gradients:
As an edge computing product, the automotive AI Box's initial important purpose in design is to provide computing power. The current AI Box on the market boasts 30-200TOPS, which is enough to meet the computing power required by models with 1-8B parameters.
Among them, the mainstream AI Box is built based on NVIDIA's modules (such as Jetson AGX Orin, Jetson Orin NX, Jetson Orin Nano), with a computing power of 200-275TOPS. It mainly handles tasks such as agent scenario services and multi-modal data processing. For example, the AI Box launched by ThunderSoft, Geely, and NVIDIA is an OEM AI Box with 200TOPS computing power and 205GB/s bandwidth, which is enough to meet the computing power required by agent matrix applications in scenarios such as welcome interaction, active recommendation, enhanced sentry, HPA and GUI interaction.
In addition, ThunderSoft's AI Box not only has built-in Aqua Drive OS and NVIDIA DriveOS, but also built-in AI Agent (such as Sentinel Agent), which can quickly apply the three major capabilities of OS layer computing power allocation, model scheduling, and scenario adaptation to agent scenarios to achieve millisecond-level response to multi-modal data.
The application of AI Box starts from "cockpits of mid-to-low-end vehicle models" + "AM"
Key Topics Covered:
1 Status Quo and Trends of Automotive AI Box
1.1 Overview of AI Box
1.2 Development Trends of AI Box
2 Solutions of OEM AI Box Suppliers
2.1 Thundersoft
2.2 BICV
2.3 Huawei
2.4 TWOWIN Technology
2.5 ADAYO
2.6 AAEON
2.7 Inspur
2.8 ARBOR
2.9 PlanetSpark
2.10 STONKAM
2.11 King Histrong
2.12 Other Vendors
- Lenovo Vehicle Computing, ArcherMind AI Box
- AI Box of MeiG Smart Technology
- TINNOVE AI Box
3 AM AI Box
3.1 Banma SmartDrive
3.2 Dongfeng Honda
3.3 Other Vendors
- MXNAVI AI Box
- Gallop Sprite AI Box
- Jinchisheng AI Box
- Boyi Electronic AI Box
4 AI Box Parts Suppliers: Comparison of Modules from Different Suppliers and Application Strategies in AI Box
4.1 NVIDIA
4.2 AMD
4.3 MediaTek
4.4 Rockchip
4.5 Huawei
4.6 UNISOC
4.7 Samsung
4.8 Micron
4.9 SK Hynix
4.10 CXMT
4.11 ArcherMind Technology
For more information about this report visit https://www.researchandmarkets.com/r/xsjkpn
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