汽車AI盒子（2026）

AI盒子是邊緣AI部署的 "加速器" 。

"邊緣-雲協同" 方案已成為汽車AI部署領域的共識。邊緣AI負責處理需要高頻、即時隱私保護的任務（例如本地資料處理、即時感知和快速回應），而雲端AI則負責處理複雜的推理、模型最佳化以及大規模資料儲存和分析。這種邊緣AI和雲AI之間清晰的角色劃分降低了部署難度，提高了AI的運作效率。

與雲端AI相比，邊緣AI在即時效能和隱私保護方面具有固有優勢。然而，隨著AI能力的不斷發展，邊緣AI也面臨一些特有的新挑戰。

舊款車型的運算能力不足以支援新的AI功能：隨著AI代理等複雜功能的加入，原車整合晶片的固定運算能力往往無法滿足日益增長的演算法需求。

現有模型的效能無法跟上不斷湧現的新場景：人工智慧應用場景的複雜性和數量都在增加。原有的車輛邊緣人工智慧模型在經過剪枝和量化後性能有限，無法準確推理和預測新增的複雜場景。

車載人工智慧盒子解決了這兩個挑戰：首先，它們採用高效能晶片提升車輛的運算能力上限，為實現新的演算法和功能提供充足的運算能力。其次，它們預先安裝了基礎人工智慧演算法框架，維持即時邊緣推理，並支援透過雲端提供最佳化的輕量級模型更新套件。這使得邊緣人工智慧能力能夠持續演進，即使在複雜場景下，也能憑藉內部強大的運算能力提升人工智慧的推理和決策能力。

以輔助運算能力為例，目前的邊緣人工智慧模型通常包含10億到80億個參數，不同參數數量的底層模型對運算能力的需求有明顯的趨勢。

作為邊緣運算產品，車載AI盒子的設計首要目標是提供強大的運算能力。目前市面上的AI盒子擁有30-200 TOPS的運算能力，完全能夠滿足參數量在10億到80億之間的模型所需的計算量。

其中，主流的AI盒子是基於NVIDIA模組（例如Jetson AGX Orin、Jetson Orin NX和Jetson Orin Nano）構建，提供200-275 TOPS的運算能力。它們主要處理諸如智能體場景服務和多模態資料處理等任務。例如，ThunderSoft、吉利和NVIDIA共同開發的AI盒子是一款OEM AI盒子，擁有200 TOPS的計算性能和205 GB/s的頻寬，完全能夠滿足迎賓交互、主動推薦、增強監控、HPA和GUI交互等場景下基於智能體的應用所需的計算能力。

除了搭載 Aqua Drive OS 和 NVIDIA DriveOS 系統外，ThunderSoft 的 AI 盒子還整合了 AI 代理（例如 Sentinel Agent），能夠在操作系統層面快速應用三大功能：算力分配、模型調度和場景自適應，從而實現對多模態資料的毫秒響應。

本報告從場景需求、產品配置和產業鏈整合等方面探討了汽車 AI 盒子的當前應用現狀，並展望了汽車 AI 盒子的未來發展趨勢。

目錄

定義

第一章：汽車AI盒子的現況與發展趨勢

• AI盒子概述
• AI盒子發展趨勢

第二章：OEM AI盒子供應商解決方案

• AI盒子對比（1）
• AI盒子對比（2）
• AI盒子對比（3）
• Thundersoft
• BICV
• 華為
• TWOWIN Technology
• ADAYO
• AAEON
• 浪潮
• ARBOR
• PlanetSpark
• STONKAM
• King Histrong
• 其他供應商

第三章：AM AI盒子

• 半馬智能驅動
• 東風本田
• 其他供應商

第四章：AI盒子組件供應商

• 供應商模組及AI盒子應用策略對比
• NVIDIA
• AMD
• 聯發科
• 瑞芯微
• 華為
• 紫光展銳
• 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