<img src="https://ml.globenewswire.com/Resource/Download/908fb457-7f8e-4a08-9081-5565e3dfb3d7?size=2" nborder="0" align="left" hspace="5" vspace="5" title="22157.jpg" alt="Company Logo"/>Automotive cloud service platforms are progressing with an architecture upgrade and improved computing power, shifting focus from hardware to service quality. Cloud providers leverage algorithm optimization and cloud-native AI as competitive edges, while OEMs utilize multi-cloud strategies for cost-effective core business implementation. Device-cloud collaboration enhances cockpit and vehicle-road-cloud experiences, optimizing resource use and real-time interactions.Dublin, May 13, 2026 (GLOBE NEWSWIRE) -- The <a href="https://www.researchandmarkets.com/reports/6232007/automotive-cloud-service-platform-research-report?utm_source=GNE&amp;utm_medium=PressRelease&amp;utm_code=vpr56r&amp;utm_campaign=2250697+-+Automotive+Cloud+Service+Platform+Research+Report+2026+Featuring+8+Automotive+Public+Cloud+Platforms+and+Cloud+Platform+Layout+of+15+OEMs&amp;utm_exec=chdomspi" rel="nofollow" target="_blank" title="Automotive Cloud Service Platform Research Report, 2026">"Automotive Cloud Service Platform Research Report, 2026"</a> report has been added to ResearchAndMarkets.com's offering. 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 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. 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: <ul><li>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.</li><li>Reduced memory access enables faster inference, with zero additional overhead in the inference pipeline.</li><li>The quantization speed is 100,000 to 1 million times faster than PQ/RabitQ.</li><li>According to the results released by Google, the TurboQuant curve achieves nearly lossless performance in long context compression (score reaches 0.997).</li></ul> 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: <ul><li>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.</li><li>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.</li><li>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.</li><li>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.</li></ul> 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. 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. 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". 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. Key Topics Covered: 1 Overview and Trends of Automotive Cloud Services 1.1 Overview of Automotive Cloud Service Industry 1.2 Automotive Cloud Service Demand 1.3 Trends in Cooperation between OEMs and Cloud Providers 1.4 Cloud Native 1.5 Automotive Cloud Technology Trends 2 Automotive Cloud Service Solutions 2.1 Autonomous Driving Cloud 2.2 Internet of Vehicles Cloud 2.4 Digitization 2.5 Cloud Data Closed Loop 2.6 AI Cloud 2.7 Cloud Information Security 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) 4.2 Microsoft Cloud Azure 4.3 Google Cloud Platform (GCP) 4.4 Huawei Automotive Cloud 4.5 Baidu Automotive Cloud 4.6 Alibaba Automotive Cloud 4.7 Tencent Automotive Cloud 4.8 ByteDance Automotive Cloud 5 OEM Cloud Platform Layout 5.1 Geely 5.2 Xpeng 5.3 Li Auto 5.4 NIO 5.5 FAW 5.6 Changan 5.7 Great Wall Motor 5.8 SAIC 5.9 GAC 5.10 Dongfeng Motor 5.11 BAIC 5.12 BMW 5.13 Mercedes-Benz 5.14 Stellantis 5.15 GM For more information about this report visit <a href="https://www.researchandmarkets.com/reports/6232007/automotive-cloud-service-platform-research-report?utm_source=GNE&amp;utm_medium=PressRelease&amp;utm_code=vpr56r&amp;utm_campaign=2250697+-+Automotive+Cloud+Service+Platform+Research+Report+2026+Featuring+8+Automotive+Public+Cloud+Platforms+and+Cloud+Platform+Layout+of+15+OEMs&amp;utm_exec=chdomspi" rel="nofollow" target="_blank" title="">https://www.researchandmarkets.com/r/xy72ed</a> About ResearchAndMarkets.com ResearchAndMarkets.com is the world's leading source for international market research reports and market data. 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Automotive Cloud Service Platform Research Report 2026 Featuring 8 Automotive Public Cloud Platforms and Cloud Platform Layout of 15 OEMs

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