汽车云端服务平台（2026）

2026年，车联网（IoV）产业每天将产生Petabyte的数据，车辆后端系统每天将自动与云端伺服器通讯10到100次。随着VLA模型和驾驶座代理的迭代周期进一步缩短，对云端运算的稳定性、低延迟和储存效率的需求将会增加，从而推动云端基础设施的转型从「规模主导」转向「价值主导」。

对于云端服务供应商而言，竞争的重点已从「硬体互补性」转向「提升服务品质」。演算法优化、云端原生人工智慧、协同调度和安全合规性正成为关键的竞争优势。

对于OEM厂商而言，多重云端策略使其能够合理利用各种云端供应商的生态系统和技术优势，从而实现“降低成本和提高效率”，确保即时云端服务的稳定性，加速自动驾驶、智慧驾驶座和出行服务等核心业务的发展，并建立差异化的竞争优势。

云端服务供应商的基础设施重点正在转向「提高品质和效率」。

2024年，汽车云端服务供应商面临「晶片短缺和算力短缺」的双重困境。为了满足大规模人工智慧模型和自动驾驶辅助系统（NOA）整合到车辆中所带来的运算能力激增需求，云端服务供应商增加了硬体投资，增加了伺服器和GPU的数量。一些服务提供者甚至开始自主研发晶片。

到 2026 年，随着通用晶片产能紧张状况的逐步缓解，以及云端运算算力利用效率的演算法优化不断进步（虚拟化、分段和池化技术日趋成熟），汽车云端基础设施将不再盲目追求硬体扩张，而是将重点放在提高下一代汽车云服务解决方案的运算能力利用效率、稳定性和适应性上。

以Google云端和阿里云等云端服务供应商为例，它们在 2026 年的云端基础设施解决方案将专注于利用新演算法来提高现有云端基础设施的效率，并透过应用新的伺服器架构来优化云端丛集的稳定性。

1. 谷歌的新演算法提高了云端运算丛集的效率。

谷歌于2026年初发布了名为TurboQuant的演算法。该演算法透过量子压缩和智慧快取技术，有效降低了储存需求，并加快了推理处理速度。它针对汽车场景的轻量级运算需求进行了最佳化，解决了「储存硬体不足限制运算能力利用」的问题，并带来了以下优势：

在 KV 快取量化中，我们实现了近乎无损的精度，同时保持了每个通道 3.5 位元的等效精度，将所需的储存容量减少到原生 16 位元格式的五分之一以下。

减少记忆体存取可以加快推理速度，并且不会在推理管道中产生额外的开销。

量化速度比 PQ/RabitQ 快 100,000 到 1,000,000 倍。

根据Google公布的结果，TurboQuant 曲线在长上下文压缩方面实现了近乎无损的性能（得分达到 0.997）。

2. 阿里云等中国云端服务供应商正在采用超级节点架构来提高其运算丛集的运作效率。

在中国云端服务供应商中，阿里云、百度云和华为云已于2025年采用超级节点伺服器架构，以优化丛集稳定性。这提高了推理效率和丛集稳定性，从而提升了解决方案的整体性价比。

本报告对中国汽车产业进行了分析，概述了汽车云端服务的现状和发展趋势，并介绍了各公司的解决方案、基础设施和平台。

目录

第一章：汽车产业云端服务的概述与发展趋势

• 汽车云端服务产业概览
• 汽车业对云端服务的需求
• OEM厂商与云端服务供应商合作的趋势
• 云端原生
• 汽车领域云端运算技术的发展趋势

第二章：面向汽车产业的云端服务解决方案

• 自动驾驶云
• 物联网云
• V2X云
• 数位化
• 云端资料闭合迴路
• AI云
• 云端资讯安全
• SOA 云

第三章：云端平台基础设施

• 汽车云产业链
• 资料中心：分发
• 资料中心：公共云端资料中心布局
• 云端伺服器
• 伺服器晶片：技术蓝图
• 伺服器晶片：晶片供应商
• 云端服务供应商的专有晶片（1）至（5）

第四章 汽车产业公共云端平台

• Amazon Web Services（AWS）
• Microsoft Cloud Azure
• Google Cloud Platform（GCP）
• Huawei Automotive Cloud
• Baidu Automotive Cloud
• Alibaba Automotive Cloud
• Tencent Automotive Cloud
• ByteDance Automotive Cloud

第五章：OEM云端平台布局

• Geely
• Xpeng
• Li Auto
• NIO
• FAW
• Changan
• Great Wall Motor
• SAIC
• GAC
• Dongfeng Motor
• BAIC
• BMW
• Mercedes-Benz
• Stellantis
• GM

Research on automotive cloud service platform: with architecture upgrade and computing power improvement, cloud services enter a new stage

In 2026, the Internet of Vehicles industry generates petabytes of data in a single day, and the vehicle backend system communicates automatically with the cloud server ten to hundreds of times a day. As the iteration cycle of VLA models and cockpit agents is further shortened, higher requirements are placed on the stability, low latency, and storage efficiency of cloud computing power, promoting the transformation of cloud infrastructure from "scale-driven" to "value-driven".

For cloud providers, the focus of competition has shifted from "complementing hardware" to "improving service quality". Algorithm optimization, cloud-native AI, collaborative scheduling, and security compliance have become competitive edges;

For OEMs, through a multi-cloud strategy and rational use of the ecosystem and technical advantages of different cloud providers, they can achieve "cost reduction and efficiency improvement", ensure the stability of real-time cloud services, and accelerate the implementation of core businesses such as autonomous driving, intelligent cockpits, and mobility services, building differentiated competitive edges.

The focus of cloud providers' infrastructure shifts to "improving quality and efficiency".

In 2024, automotive cloud providers found themselves trapped in a dilemma of "chip shortages and insufficient computing power." Cloud providers ramped up their hardware investments to stack servers and GPUs to meet the surging demand for computing power driven by the integration of AI large models and NOA (Navigate on Autopilot) into vehicles. Some providers also began to develop chips in-house.

In 2026, as the tight production capacity of general-purpose chips gradually eases and algorithms continue to optimize utilization efficiency of cloud computing power (virtualization, segmentation, and pooling technologies become more mature), automotive cloud infrastructure will no longer blindly pursue the expansion of hardware, but will center on improving utilization efficiency, stability, and adaptability of computing power as the focus of developing next-generation automotive cloud service solutions.

Taking cloud providers such as Google Cloud and Alibaba Cloud as examples, their cloud infrastructure solutions in 2026 focus on improving the efficiency of existing cloud infrastructure with new algorithms and applying new server architectures to optimize the stability of cloud clusters.

1.Google's new algorithm improves cloud computing cluster efficiency

Google introduced the algorithm TurboQuant in early 2026. With quantitative compression and intelligent caching technology, it effectively lowers storage requirements and speeds up inference. It can adapt to the lightweight computing power requirements of automotive scenarios and solve the problem of "insufficient storage hardware restricting the utilization of computing power". It offers the following benefits:

For KV Cache quantization, 3.5 bits per channel achieves near-lossless precision with equivalent accuracy, reducing the storage required by more than 5x compared to the native 16-bit format.

Reduced memory access enables faster inference, with zero additional overhead in the inference pipeline.

The quantization speed is 100,000 to 1 million times faster than PQ/RabitQ.

According to the results released by Google, the TurboQuant curve achieves nearly lossless performance in long context compression (score reaches 0.997).

2.Chinese cloud providers such as Alibaba Cloud apply super-node architectures to improve the operating efficiency of computing clusters.

Among Chinese cloud providers, Alibaba Cloud, Baidu Cloud, and Huawei Cloud launched super-node server architectures that optimize cluster stability in 2025, optimizing inference efficiency and cluster stability, and improving the cost-effectiveness of the entire solutions:

Alibaba Cloud

Alibaba Cloud released Panjiu AI Infra 2.0 AL128 super node servers at the 2025 APSARA Conference. Through ScaleUp interconnection within the super node, they shorten the completion time of E2E inference tasks and improve foundation model inference experience for users. One of the features of such servers lies in ScaleUp interconnection, a technology that caters to modern GPU design, including:

Native memory semantics: Direct access to the computing core of the GPU is allowed, and it is easy to mount to the SoC bus via the interface. There is no conversion overhead and intrusive design for the computing core.

Ultimate performance: Extremely high bandwidth (the entire chip can reach TB/s) and extremely low latency can be achieved. In addition to the high message efficiency of the protocol, excellent performance under high load is also required.

Minimalist implementation: Chip area and cost are minimized, allowing valuable resources and power consumption to be reserved for the computing power and on-chip memory of GPU.

Highly reliable link: In a very high-density SerDes environment, high availability is ensured through a high-performance physical layer and link-level retransmission and fault isolation mechanisms.

Huawei

Huawei has released the next-generation AI data center architecture - CloudMatrix and the mass production product - CloudMatrix384, which breaks through the traditional CPU-centric hierarchical design and supports direct high-performance communication between all heterogeneous system components (including NPU, CPU, DRAM, SSD, NIC and domain-specific accelerators), realizing the transformation of the resource supply model from the server level to the matrix level.

In August 2025, Changan Tops AD adopted Huawei Cloud's CloudMatrix384 super node solution". Based on the CloudMatrix384 super node and Huawei Cloud's high-bandwidth and large-capacity storage cluster, Changan Automobile has achieved efficient training of its autonomous driving model, and adaptation to various autonomous driving models such as VLA and end-to-end models.

Baidu

Relaying on Kunlunxin, a super node server architecture was released. This solution achieves super single-node performance. Its 32-GPU/64-GPU configuration uses faster in-machine communication to increase inter-GPU interconnection bandwidth by 8 times, single-machine training performance by 10 times, and single-GPU inference performance by 13 times, which can support large-scale VLA training and promotion.

Device-cloud collaboration technology optimizes cockpit and vehicle-road-cloud scenario experience.

From 2025 to 2026, device-cloud collaboration technology serves as one of the technical bases to accelerate the penetration into cockpit and vehicle-road-cloud scenarios. With the complementary model of "cloud computing power empowerment + automotive real-time response", it will solve problems such as unsmooth cockpit interaction and vehicle-road-cloud system effects that are not as good as expected, and optimize user experience.

1.Cockpit scenario

In 2026, the cockpit device-cloud collaborative architecture upgrades capabilities through the combined approach of "cloud foundation model optimization + vehicle lightweight model execution". The cloud undertakes high-load computing and inference tasks, including complex semantic understanding, multi-turn dialogue tracking, massive knowledge base data invocation, and other tasks requiring high computing power. The vehicle is in charge of real-time response, low-latency interaction, and privacy protection. With technologies such as edge node sinking, the end-to-end latency is controlled within 500 milliseconds to meet user needs. Cloud IVI is a typical application of device-cloud collaboration in cockpit scenarios.

For example, the Aion Cloud IVI released by GAC and Huawei in September 2025 uses vehicle-cloud intelligent collaboration to reconstruct the cockpit computing power allocation logic: all computing and rendering tasks are handed over to the cloud, and the local IVI is only responsible for interaction and display. The IVI local computing only consumes 0.02-0.03TFLOPS, which greatly reduces the consumption of automotive computing power. This not only ensures a smooth experience of the new IVI system, but also solves the problem of the old vehicle upgrade: there is no need to replace hardware, and smooth intelligent interaction can be achieved even with mid- to low-end chips.

In addition to saving computing resources, this cloud IVI also takes advantage of cloud resources to:

Complete cloud ecosystem aggregation, open up 20,000+ cloud applications, and support the flow of mobile applications to IVI.

Speed up the OTA frequency; all application and system updates are completed in the cloud, and the latest version can be updated in half a day, allowing cockpit functions to always remain "cutting-edge".

2.Vehicle-road-cloud scenario

In the vehicle-road-cloud scenario, the core value of device-cloud collaboration lies in opening up the data links between vehicles, roadside equipment and cloud platforms, and building a complete collaborative closed loop of "vehicle perception, roadside blind spot coverage, and cloud scheduling".

The cloud is responsible for core tasks such as data fusion, macro traffic flow prediction, and global scheduling optimization. Through multi-dimensional data fusion, intelligent allocation of mobility resources is realized. The cloud control platform adopts a two-level architecture of "edge cloud + zonal cloud" to achieve hierarchical processing and global optimization.

Edge computing nodes serve as vehicle-road connection hubs, ensuring end-to-end latency of <=10 milliseconds and focusing on real-time data processing and local scheduling.

In August 2025, Dongfeng eπ007 realized the technology of optimizing the smart parking function with vehicle-road-cloud collaboration technology. The technical path is "cloud scheduling + parking lot allocation + vehicle execution". This technology can increase the parking space utilization rate by 45% and increase the number of vehicles parked per unit area by 1.8 times. Thanks to parking lot sensors and cloud technology, Dongfeng eπ007 does not require manual operation after running into the parking lot. The parking lot equipment can instantly recognize license plates, compressing the entry time to within 15 seconds.

Table of Contents

1 Overview and Trends of Automotive Cloud Services

• 1.1 Overview of Automotive Cloud Service Industry
• Definition of Automotive Cloud
• China's Automotive Cloud Market Size (1)-(3)
• Automotive Cloud Platform Classification: Comparison of Features
• Automotive Cloud Platform Classification: Three Different Service Types
• Automotive Cloud Platform Classification: Comparison of Typical Cloud Providers in China
• Competitive Landscape of Automotive Cloud Services (1)-(2)
• China's Automotive Cloud Business Models
• 1.2 Automotive Cloud Service Demand
• Automotive Cloud Application Scenarios
• China's Automotive Cloud Service Demand: Three Stages
• China's Automotive Cloud Service Demand: Transformation Characteristics
• China's Automotive Cloud Service Demand: Status Quo
• China's Automotive Cloud Service Demand Sub-scenarios: AI Foundation Models
• China's Automotive Cloud Service Demand Sub-scenarios: Autonomous Driving Cloud Platforms
• China's Automotive Cloud Service Demand Sub-scenarios: Deep Integration of Cloud Platform Tool Chains
• 1.3 Trends in Cooperation between OEMs and Cloud Providers
• Automotive Cloud Business Models
• OEM Cloud Platform Selection Trends (1):
• OEM Cloud Platform Selection Trends (2):
• Summary of Cloud Capabilities Required by OEMs
• 1.4 Cloud Native
• Cloud Native: Concept
• Cloud Native: Main Technologies and Advantages
• Cloud Native: Application Scenarios
• Cloud Native System Technology Base: Vehicle-Cloud Collaboration
• Cloud Native System Technology Base: Data Lake
• Cloud Native: Security Evolution
• Cloud Native Application Cases of Suppliers: (1)-(3)
• Cloud Native Application Cases of OEMs: (1)-(6)
• Cloud Native Application Cases of OEMs: Summary
• 1.5 Automotive Cloud Technology Trends
• AI Cloud Trends (1):
• AI Cloud Trends (2): Cloud Native AI Becomes A New Paradigm
• Edge Cloud Trends:
• Internet of Vehicles Cloud Trends:
• Cloud Computing Trends:

2 Automotive Cloud Service Solutions

• 2.1 Autonomous Driving Cloud
• Autonomous Driving's Demand for Cloud
• Autonomous Driving Cloud Application Scenarios
• Autonomous Driving Cloud Platforms: Enable Three Types of Functions
• Examples of Autonomous Driving Cloud Service Providers: (1)-(2)
• World Models Empower the Actual Value of Autonomous Driving Cloud
• 2.2 Internet of Vehicles Cloud
• Application Scenarios
• Demand for Cloud (1)-(3)
• Examples of Cloud Service Providers: (1)-(2)
• 2.3 V2X Cloud
• Overview
• Service Architecture: Common Architecture
• Service Architecture: Architecture Segments
• Automotive Cloud Computing: Six Services
• Automotive Cloud Computing: Problems And Solutions
• Examples of Cloud Service Providers: (1)-(2)
• 2.4 Digitization
• Overview
• Demand for Cloud
• 2.5 Cloud Data Closed Loop
• Overview
• Role of Cloud Platform in Data Closed Loop: (1)-(3)
• Cloud Platform Data Closed Loop Cases: (1)-(5)
• 2.6 AI Cloud
• Application Scenarios
• Reference Architecture
• Application of AI in IaaS, PaaS and MaaS
• Integration of AI Cloud Computing and Intelligent Computing
• Cloud AI Accelerator
• Collaborative Deployment of AI Cloud and Edge
• 2.7 Cloud Information Security
• Internet of Vehicles Security Challenges
• Cloud Security Scenarios
• Cloud Information Threats
• Cloud Information Security Architecture
• Cloud Security Strategies: (1)-(5)
• Typical Cases of Cloud Security: (1)-(4)
• 2.8 SOA Cloud
• Cloud Native in SOA
• Cases (1)-(2)

3 Cloud Platform Infrastructure

• 3.1 Automotive Cloud Industry Chain
• 3.2 Data Centers: Distribution
• 3.2 Data Centers: Public Cloud Data Center Layout
• 3.3 Cloud Servers
• 3.4 Server Chips: Technical Roadmap
• 3.4 Server Chips: Chip Suppliers
• 3.5 Self-Developed Chips of Cloud Providers: (1)-(5)

4 Automotive Public Cloud Platforms

• 4.1 Amazon Web Services (AWS)
• Overview of Automotive Cloud Business
• Regional Distribution
• Automotive Industry Layout
• AWS for Automotive
• Solutions for Software-Defined Vehicles
• Internet of Vehicles Data Lake
• Autonomous Driving Data Lake
• SDV Cloud Development Architecture
• Cloud Digital Operation
• Cloud Data Management
• Automotive Industry Customers
• Supply Relationship
• Cooperation Cases: (1)-(7)
• 4.2 Microsoft Cloud Azure
• Automotive Solutions
• Internet of Vehicles Cloud Platform
• Microsoft Connected Vehicle Platform (MCVP): Business Models and Major Customers
• Microsoft Connected Vehicle Platform (MCVP): Ecosystem Partners
• Cooperative Auto Parts Companies
• Cooperative OEMs
• 4.3 Google Cloud Platform (GCP)
• GCP
• Cooperation with Qualcomm
• 4.4 Huawei Automotive Cloud
• Business
• Automotive Solutions
• Internet of Vehicles solutions
• Autonomous Driving Development Solution: Ascend Platform
• Mobile Mobility Solutions
• Automotive Simulation Solutions
• Digital Intelligent Platform Solutions
• Digital Marketing Solutions
• Overseas Business Solutions (1)-(5)
• Cooperative Customers (1)-(2)
• Internet of Vehicles Cloud Technology: Serverless Technology
• Infrastructure
• Dynamics: FAW-Volkswagen Manufacturing Cloud
• 4.5 Baidu Automotive Cloud
• Baidu Cloud Autonomous Driving Solutions (1)-(4)
• Baidu Internet of Vehicles Cloud
• Baidu Internet of Vehicles Cloud: OEM Customers
• Baidu V2X Cloud
• Baidu Cloud Data Closed Loop Solutions
• Baidu Cloud Data Annotation Solutions
• Baidu Cloud Security System
• Baidu Autonomous Driving Cloud's Support Base for VLA
• Baidu Autonomous Driving Cloud's Support Base for VLA (1): Computing Power
• Baidu Autonomous Driving Cloud's Support Base for VLA (2): Data
• Baidu Autonomous Driving Cloud's Support Base for VLA
• 4.6 Alibaba Automotive Cloud
• Business
• Industrial Capabilities
• Technical Base: (1)-(4)
• Main Customers
• Internet of Vehicles Security Solution: Cloud-Network-Edge Integrated Joint Defense
• Overseas Services
• 4.7 Tencent Automotive Cloud
• Business
• Architecture: AI Cloud
• Cockpit Function Layout
• "Cloud-Map Integration" Layout
• Architecture: Next-Generation Data Closed Loop
• Autonomous Driving Cloud
• Intelligent connected cloud
• Capability
• Security mechanism
• Automakers served
• 4.8 ByteDance Automotive Cloud
• Business
• System architecture
• System Architecture: Autonomous Driving Tool Chain
• Important Nodes in Business Development
• Ecology
• Computing Capabilities of ByteDance Cloud
• Multi-Cloud Disaster Tolerance Architecture of Volcano Engine: (1)-(5)
• Latest Cooperation with OEMs: (1)-(2)

5 OEM Cloud Platform Layout

• OEM Solution Comparison
• 5.1 Geely
• Geely's Cloud Platform Strategy
• Geely's Digital Transformation Strategic Plan
• Geely Group's Cloud Platform Solution and Planning
• Geely's Cooperation Cases (1)-(3)
• Geely Cloud IVI
• Geely's Internet of Vehicles Cloud Platform Technology
• ZEEKR's Internet of Vehicles Cloud Cooperation Case
• The Cloud Smart Center of ZEEKR AI BMS Has Four Major Protection Functions
• 5.2 Xpeng
• Cloud Platform
• Cooperation with Alibaba Cloud
• 5.3 Li Auto
• Cloud Platform Layout
• IoV Cloud
• Data Storage Solution
• Application of Device-Cloud Integration Architecture in Functional Safety (1): Problems
• Application of Device -Cloud Integration Architecture in Functional Safety (2): Solutions
• Application of Device -Cloud Integration Architecture in Functional Safety (3): Effects
• Internet of Vehicles Cloud Data Analysis Platform (1): Problems
• Internet of Vehicles Cloud Data Analysis Platform (2): Architecture and Characteristics
• Internet of Vehicles Cloud Data Analysis Platform (3): Performance and Guarantee
• Internet of Vehicles Cloud Data Analysis Platform (4): History
• Internet of Vehicles Cloud Data Analysis Platform (5): Usage Effects
• Internet of Vehicles Cloud Data Analysis Platform (6): Scenarios
• Internet of Vehicles Cloud Data Analysis Platform (7): Future Planning
• Internet of Vehicles Architecture Based on NDN Helps Vehicle-Cloud Collaboration
• Vehicle-Cloud Collaborative Architecture of Data Observability Platform
• Cloud AI Is Used in Manufacturing
• 5.4 NIO
• Hybrid Cloud
• Energy Cloud: AI Order Prediction and Edge-Cloud Collaboration Strategy
• Cloud Infrastructure Team
• 5.5 FAW
• Cloud Platform Layout
• Hongqi Smart Cloud
• Local Data Centers
• Cooperation Cases (1)-(4)
• 5.6 Changan
• Digital Path: Cloud Migration Phase
• Digital Path: Data Governance Phase
• Digital Path: Intelligence Enablement Phase
• Cloud Platform Big Data
• Autonomous Vehicle Cloud Big Data Processing Architecture
• Internet of Vehicles Cloud and R&D Cloud
• Device-Cloud Integration SDA
• Device-Cloud Integration Service Ecosystem
• Automotive Cloud Platform Partners (1)-(2)
• Vehicle-Road-Cloud Integrated Project
• Avatr's Private Cloud Deployment
• 5.7 Great Wall Motor
• Intelligent Cloud
• Great Wall Motor and Huawei Cloud
• Internet of Vehicles Cloud Bus Technology Uses Alibaba Cloud Services
• AWS and Alibaba Cloud Are Used Overseas
• Automotive AI Cloud
• 5.8 SAIC
• Cloud Business Layout
• Cloud Products and Services
• Cloud Platform: (1)-(2)
• Autonomous Driving Cloud
• Fin-Shine Combines the Data Process of Cloud Foundation Models
• Fin-Shine's Intelligent Connectivity Cloud
• Fin-Shine's Cloud Cooperation Cases
• Cloud Product Technical Route and Security Route
• Overseas Cooperation with AWS
• Z-One Completes CVIS Energy Saving through Cloud Mechanism
• Cloud Platform Security Protection
• 5.9 GAC
• Cooperate with Tencent in Internet of Vehicles Cloud/GAC Autonomous Driving Cloud
• Cooperate with ByteDance Cloud in Digital Cloudification
• Cooperation with Alibaba Cloud
• DevOpsPaaS Service Platform: Three Construction Stages
• DevOpsPaaS Service Platform: Features and Effects
• DevOpsPaaS Service Platform: Features
• DevOpsPaaS Service Platform: Effects
• Cloud IVI
• 5.10 Dongfeng Motor
• Vehicle-Road-Cloud Integration Projects (1)
• Vehicle-Road-Cloud Integration Projects (2)
• 5.11 BAIC
• Vehicle-Road-Cloud Integration
• Internet of Vehicles Cloud Adopts Alibaba Cloud
• 5.12 BMW
• AWS Cloud and Microsoft Azure Cloud
• AWS Cloud
• Microsoft Azure Cloud
• 5.13 Mercedes-Benz
• Access AI through Microsoft Azure Cloud
• Optimize AI with Google Cloud
• Use AWS Cloud for IT Management
• Cloud Technology
• 5.14 Stellantis
• Cooperation History
• Cooperation Cases with AWS Cloud (1)
• Cooperation Cases with AWS Cloud (2): VEW Platform Development
• 5.15 GM