汽车AI盒子（2026）

AI盒子是边缘AI部署的 "加速器" 。

"边缘-云协同" 方案已成为汽车AI部署领域的共识。边缘AI负责处理需要高频、即时隐私保护的任务（例如本地资料处理、即时感知和快速回应），而云端AI则负责处理复杂的推理、模型最佳化以及大规模资料储存和分析。这种边缘AI和云AI之间清晰的角色划分降低了部署难度，提高了AI的运作效率。

与云端AI相比，边缘AI在即时效能和隐私保护方面具有固有优势。然而，随着AI能力的不断发展，边缘AI也面临一些特有的新挑战。

旧款车型的运算能力不足以支援新的AI功能：随着AI代理等复杂功能的加入，原车整合晶片的固定运算能力往往无法满足日益增长的演算法需求。

现有模型的效能无法跟上不断涌现的新场景：人工智慧应用场景的复杂性和数量都在增加。原有的车辆边缘人工智慧模型在经过剪枝和量化后性能有限，无法准确推理和预测新增的复杂场景。

车载人工智慧盒子解决了这两个挑战：首先，它们采用高效能晶片提升车辆的运算能力上限，为实现新的演算法和功能提供充足的运算能力。其次，它们预先安装了基础人工智慧演算法框架，维持即时边缘推理，并支援透过云端提供最佳化的轻量级模型更新套件。这使得边缘人工智慧能力能够持续演进，即使在复杂场景下，也能凭藉内部强大的运算能力提升人工智慧的推理和决策能力。

以辅助运算能力为例，目前的边缘人工智慧模型通常包含10亿到80亿个参数，不同参数数量的底层模型对运算能力的需求有明显的趋势。

作为边缘运算产品，车载AI盒子的设计首要目标是提供强大的运算能力。目前市面上的AI盒子拥有30-200 TOPS的运算能力，完全能够满足参数量在10亿到80亿之间的模型所需的计算量。

其中，主流的AI盒子是基于NVIDIA模组（例如Jetson AGX Orin、Jetson Orin NX和Jetson Orin Nano）构建，提供200-275 TOPS的运算能力。它们主要处理诸如智能体场景服务和多模态资料处理等任务。例如，ThunderSoft、吉利和NVIDIA共同开发的AI盒子是一款OEM AI盒子，拥有200 TOPS的计算性能和205 GB/s的频宽，完全能够满足迎宾交互、主动推荐、增强监控、HPA和GUI交互等场景下基于智能体的应用所需的计算能力。

除了搭载 Aqua Drive OS 和 NVIDIA DriveOS 系统外，ThunderSoft 的 AI 盒子还整合了 AI 代理（例如 Sentinel Agent），能够在操作系统层面快速应用三大功能：算力分配、模型调度和场景自适应，从而实现对多模态资料的毫秒响应。

本报告从场景需求、产品配置和产业链整合等方面探讨了汽车 AI 盒子的当前应用现状，并展望了汽车 AI 盒子的未来发展趋势。

目录

定义

第一章：汽车AI盒子的现况与发展趋势

• AI盒子概述
• AI盒子发展趋势

第二章：OEM AI盒子供应商解决方案

• AI盒子对比（1）
• AI盒子对比（2）
• AI盒子对比（3）
• Thundersoft
• BICV
• 华为
• TWOWIN Technology
• ADAYO
• AAEON
• 浪潮
• ARBOR
• PlanetSpark
• STONKAM
• King Histrong
• 其他供应商

第三章：AM AI盒子

• 半马智能驱动
• 东风本田
• 其他供应商

第四章：AI盒子组件供应商

• 供应商模组及AI盒子应用策略对比
• NVIDIA
• AMD
• 联发科
• 瑞芯微
• 华为
• 紫光展锐
• 三星
• 美光
• SK海力士
• CXMT
• ArcherMind Technology

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"

As of the end of January 2026, some applications of AI Box had been as follows:

From the perspective of installation, it had been mainly used in the cockpit (also available in Internet of Vehicles, but with fewer cases/applications);

From the OEM/AM perspective, it had been mainly seen in the cockpit AM (also available in the OEM market, but with fewer cases/applications), and IVI systems for old vehicle models or medium to low-end vehicle models.

AM AI Box had boasted a certain scale on the market, and had been mainly used to solve problems such as medium and low-end vehicle models' IVI lags, backward function versions, and insufficient AI functions. Such a product is connected through a USB cable to provide AI functions or supplement computing power. It had realized IVI-phone interconnection through various connection methods such as HUAWEI HiCar and CarPlay.

OEM AI Box also targets the cockpit AI service issues of medium to low-end vehicle models. It aims to superimpose the technical route of high-performance AI BOX on a medium-computing power cockpit platform to achieve rapid mass production of autonomous foundation models. Typical representatives include the AI Box of ADAYO and BICV.

For example, ADAYO's AI Box can support edge foundation models with 7 billion parameters. By providing standardized high-speed interfaces and supporting mainstream communication methods such as Gigabit Ethernet, it adapts to the current mainstream EEA and introduces foundation models without replacing the existing cockpit platform. While controlling vehicle cost and power consumption, it also reserves space for subsequent EEA upgrades.

1.Cockpit AM cases

AM AI Box already has a certain scale on the market, and is mainly used to solve problems such as low-end and medium vehicle model IVI lags, backward function versions, and insufficient AI functions. Such a product is connected through a USB cable to provide AI functions or supplement computing power. It had realized IVI-phone interconnection through various connection methods such as HUAWEI HiCar and CarPlay. Typical cases include:

Banma Zhixing AI Box

Banma Zhixing launched the Banma AI Box in June 2025. This product is deeply integrated with HUAWEI HiCar and supports IVI-phone interconnection. It also supports the iteration of Banma's latest system and can apply the Yan AI system. This product is initially adapted to the Roewe RX5 (2016-2020), and will gradually be adapted to the older IVI systems of vehicle models such as Roewe RX5, Roewe ERX5, Roewe eRX5, Roewe i6, Roewe ei6, etc.

Dongfeng Honda AI Box

Dongfeng Honda launched the AI-powered automotive cloud box, which is equipped with an 8-core automotive-grade chip that supports implicit installation and can directly apply AI foundation models to support functions such as AI voice, smart car books, short video entertainment, smart search, and image and text creation.

2.Cockpit OEM cases

NIO ET9 is equipped with N-Box, a scalable heterogeneous computing unit, which is also a type of AI Box. This product is equipped with MediaTek MT8628 and can be connected to the central computing platform.

Core configuration of AI Box: heterogeneous computing + AI framework

In summary, the automotive AI Box should meet core requirements such as automotive-grade reliability, flexible computing power supply, and AI ecological openness.

In terms of product configuration: it is necessary to build a complete technology stack of "heterogeneous computing platform + efficient AI tool chain + real-time middleware" to support complex edge AI tasks.

In terms of the industrial chain structure: upstream and downstream vendors should be committed to promoting "standardization of physical interfaces, generalization of data exchange protocols, normalization of functional safety certification, and ecologicalization of software frameworks."

including:

For example, AI Box features "heterogeneous computing + high computing power".

The computing power of mainstream automotive AI Box is 30-200TOPS. The flagship vehicle models use heterogeneous computing chip platforms. For example, the cockpit domain can use Arm Cortex-A (such as A78AE) with high-performance GPU (such as Qualcomm Adreno or high-performance ARM Mali) to support multi-screen 4K rendering and AR-HUD.

In addition, for the entry-level market, Chinese chips such as Rockchip RK3588M achieve a balance between cockpit AI interaction and basic driving-parking integration functions by integrating 6 TOPS NPUs.

In terms of software ecology, AI Box is deeply compatible with mainstream development frameworks such as PyTorch and TensorFlow, and uses ONNX as the core exchange format. At the same time, some chip vendors also provide mature underlying optimization tool chains:

NVIDIA: With the TensorRT tool chain, through operator fusion and INT8/FP8 quantization, model inference performance can be improved by several to dozens of times.

Horizon Robotics: Relying on the "OpenExplorer" platform, it provides comprehensive quantitative training (QAT) tools to ensure that the model can greatly compress the volume while controlling the accuracy loss within the effective range.

This ecological compatibility greatly reduces the threshold for algorithm migration. After developers complete model training in the cloud, they can efficiently deploy it to automotive hardware through compilation and optimization of the vendor tool chain, significantly shortening the cycle from development to production.

For instance, the "MT200 Series" launched by MeiG Smart Technology can be connected to automotive terminals for multi-modal edge processing; it can also be deployed on the roadside for intelligent traffic monitoring and CVIS. As of mid-January 2026, this product had been designated by OEMs.

The software configuration of the MT200 series:

The middleware layer is equipped with a model inference engine based on NPU hardware acceleration, supports ONNX format (inference speed increased by >=30% after optimization), and supports automated installation and version control of applications;

Its system API encapsulates underlying capabilities such as OpenCV, OpenGL, and audio and video encoding and decoding. The standardized API provides the unified device management, application deployment, and status query interface to simplify upper-layer application development;

The supporting tools integrate visual tool chains, cases and components to support rapid application construction.

Table of Contents

Definition

1 Status Quo and Trends of Automotive AI Box

• 1.1 Overview of AI Box
• From Foundation Models-Based All-in-One to AI Box: The Market Continues to Expand
• From Foundation Models-Based All-in-One to AI Box: Migration Difficulties
• Automotive AI Box Classification
• Necessary Conditions for AI Box (Hardware)
• Necessary Conditions for AI Box (Software)
• Positioning and Principle of AI Box
• Application Scenarios of Automotive AI Box
• Advantages of AI Box (1): Computing Power
• Advantages of AI Box (2)
• Architecture of AI Box (1)
• Architecture of AI Box (2)
• Software and Hardware Collaboration of AI Box (1): Foundation Model Adaptation
• Software and Hardware Collaboration of AI Box (2)
• Software and Hardware Collaboration of AI Box (3)
• 1.2 Development Trends of AI Box
• Trend 1: Product configuration features "heterogeneous computing platform + efficient AI tool chain + real-time middleware"
• Trend 2:
• Trend 3:
• Trend 4: How to install AI Box on vehicles
• Trend 5: Problems and Solutions

2 Solutions of OEM AI Box Suppliers

• AI Box Comparison (1)
• AI Box Comparison (2)
• AI Box Comparison (3)
• 2.1 Thundersoft
• Parameters
• Interconnection between AI Box and AI OS
• AI Box for AI Reasoning Framework
• Product Cooperation: ThunderX
• Product Cooperation: Geely's AI Box Planning
• Product Cooperation: Features of Geely's AI Box
• 2.2 BICV
• Parameters and Overall Planning of AI Box
• Advantages and Features of AI Box
• 2.3 Huawei
• Parameters of Edge Computing Box
• Features of Edge Computing Box
• Platform Compatibility of Edge Computing Box
• 2.4 TWOWIN Technology
• Product Layout
• AI Box: Core Module
• AI Box: Based on Jetson AGX Orin
• AI Box: Based on Jetson Orin NX
• AI Box: Based on Jetson Orin Nano
• AI Box: Based on Jetson Thor
• Test Results of Foundation Models Based on T1501 AI Box
• Application Cases of AI Box (1)
• Application Cases of AI Box (2)
• Application Cases of AI Box (3)
• AI Box for Development
• 2.5 ADAYO
• AI Box Planning (1)
• AI Box Planning (2)
• 2.6 AAEON
• Parameters of AI Box (1)
• Parameters of AI Box (2)
• Parameters of AI Box (3)
• Parameters of AI Box (4)
• Parameters of AI Box (5)
• 2.7 Inspur
• Parameters of AI Box (1)
• Parameters of AI Box (2)
• 2.8 ARBOR
• Parameters of AI Box (1)
• Parameters of AI Box (2)
• 2.9 PlanetSpark
• Parameters of AI Box
• Software Compatibility of AI Box
• 2.10 STONKAM
• Parameters of AI Box
• Application Scenarios of AI Box
• 2.11 King Histrong
• Parameters of AI Box
• Motherboard Parameters of AI Box
• 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
• Features of AI Box
• Application Scenarios of AI Box
• 3.2 Dongfeng Honda
• Features of AI Box
• Application Scenarios of AI Box
• 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
• Modules (1): Jetson AGX Orin
• Modules (2): Jetson Orin NX
• Modules (3): Jetson Orin Nano
• Modules (4): Jetson Thor
• 4.2 AMD
• Modules (1)
• Modules (2)
• 4.3 MediaTek
• Modules (1)
• 4.4 Rockchip
• Modules (1)
• Modules (2)
• Modules (3)
• 4.5 Huawei
• Modules (1)
• Modules (2)
• 4.6 UNISOC
• Modules (1)
• Modules (2)
• Comparison of Products from Different Memory Suppliers
• 4.7 Samsung
• 4.8 Micron
• 4.9 SK Hynix
• 4.10 CXMT
• Comparison of Products from Different AIOS Vendors
• 4.11 ArcherMind Technology
• Features of FusionOS
• FusionOS and Hardware Collaboration