Zuosi Auto Research released the "Research Report on Automotive Cloud Service Platforms in 2026".
In 2026, the total daily data volume generated by the automotive connected vehicle industry generally reaches the PB level, with automatic communications between vehicle backends and cloud servers occurring tens to hundreds of times a day. As the iteration cycles of VLA (Vision-Language-Action) autonomous driving models and cockpit Agents further shorten, higher requirements are placed on the stability, low latency, and storage efficiency of cloud computing power, driving the transformation of cloud infrastructure from "scale-driven" to "value-driven".
For cloud providers, the key to competition shifts from "making up for hardware quantity" to "improving service quality", with algorithm optimization, cloud-native AI, collaborative scheduling, and security compliance becoming core competencies;
For automakers, through a multi-cloud strategy and rational utilization of the respective ecosystem and technological advantages of different cloud providers, they can achieve "cost reduction and efficiency increase", ensure the stability of real-time cloud services, accelerate the implementation of core businesses such as autonomous driving, intelligent cockpits, and mobility services, and build differentiated competitive advantages.
The Key Focus of Infrastructure Services for Cloud Providers Shifts from "Quantity" to Equal Emphasis on "Quantity" and "Quality"
Image source: Zuosi Auto Research
The Focus of Cloud Providers' Infrastructure Shifts to "Improving Quality and Efficiency"
In 2024, automotive cloud providers were deeply trapped in the dilemma of "chip shortage and computing power deficit". Cloud providers successively increased hardware investment, piling up servers and GPUs to meet the surging demand for computing power brought by AI large models and the integration of NOA (Navigate on Autopilot) into vehicles. Some providers began to lay out "in-house chip manufacturing".
In 2026, as the production capacity of general-purpose chips gradually eases and algorithms continuously optimize the utilization efficiency of cloud computing power (with virtualization, partitioning, and pooling technologies becoming more mature), automotive cloud infrastructure no longer blindly pursues the expansion of hardware scale. Instead, it focuses on improving computing power utilization efficiency, stability, and adaptability as the development priority for the new generation of automotive cloud service solutions.
Taking cloud providers such as Google Cloud and Alibaba Cloud as examples, the cloud infrastructure solutions in 2026 focus on improving the efficiency of existing cloud infrastructure through new algorithms and applying new server architectures to optimize the stability of cloud clusters.
01. Google's New Algorithm Enhances the Efficiency of Cloud Computing Power Clusters
In early 2026, Google released the TurboQuant algorithm. Through quantization compression and intelligent caching technologies, it effectively reduces storage requirements and improves inference speed. It can adapt to the lightweight computing power needs of in-vehicle scenarios and optimize the problem of "insufficient storage hardware restricting the performance of computing power". Its effects include:
For KV Cache quantization, at the same precision, 3.5 bits per channel achieves completely lossless compression, requiring more than 5 times less storage space than native 16-bit;
Memory access is reduced, achieving improved inference speed with zero additional overhead in the inference pipeline;
The quantization speed is 100,000 to 1,000,000 times faster than PQ / RabitQ.
Advantages of the TurboQuant Algorithm
Data source: Google; Compiled by: Zuosi Auto Research
Performance of TurboQuant in the Needle-In-A-Haystack Long Context Retrieval Task
Image source: Google
According to the results released by Google, the TurboQuant curve achieves nearly lossless performance in long context compression (scoring 0.997).
02. Domestic Cloud Providers Such as Alibaba Cloud Apply Super Node Architecture to Improve the Operational Efficiency of Computing Power Clusters
Among domestic cloud providers, Alibaba Cloud, Baidu Cloud, Huawei Cloud, and others have all launched super node server architectures to optimize cluster stability in 2025, improving inference efficiency, cluster stability, and the cost-effectiveness of the overall solutions:
Alibaba Cloud
At the 2025 Apsara Conference, Alibaba Cloud released the Panjiu AI Infra 2.0 AL128 super node server. Through ScaleUp interconnection within the super node, it shortens the completion time of E2E (End-to-End) inference tasks and improves users' large model inference experience. One of the features of this server is the adoption of Scale Up interconnection. This technology needs to meet the requirements of modern GPU design, including:
Native memory semantics: It enables direct access by the computing cores of the GPU and is easy to mount to the SoC (System on Chip) bus via interfaces, with no conversion overhead and intrusive design for the computing cores.
Ultimate performance: It can achieve extremely high bandwidth (up to the TB/s level for the entire chip) and extremely low latency. In addition to the high packet efficiency of the protocol, it also needs to perform excellently under high loads.
Minimalist implementation: It minimizes chip area costs. By using the smallest possible chip implementation area, it leaves precious resources and power for the computing power and on-chip storage of the GPU chip.
Highly reliable links: In environments with very high SerDes density, it ensures high availability through a high-performance physical layer, combined with link-level retransmission and fault isolation mechanisms.
Topology Diagram of Alibaba Cloud Panjiu Super Node ScaleUp Interconnection
Image source: Alibaba Cloud
Huawei
Huawei released the new-generation AI data center architecture CloudMatrix and the mass-produced product CloudMatrix 384. Breaking through the traditional CPU-centric hierarchical design, it supports direct high-performance communication among all heterogeneous system components (including NPU, CPU, DRAM, SSD, NIC, and domain-specific accelerators), realizing the shift in resource supply mode from the server level to the matrix level.
In August 2025, Changan Automobile's Tianshu Autonomous Driving adopted the "Huawei Cloud CloudMatrix 384 Super Node Solution". Based on the CloudMatrix 384 super node and Huawei Cloud's high-bandwidth, large-capacity storage cluster, it achieves efficient training of Changan Automobile's autonomous driving models and adapts to various autonomous driving models such as VLA and end-to-end.
Baidu
Relying on Kunlun Xin (Kunlun Chip), it released a super node server architecture. This solution achieves ultra-strong performance for a single node. Its 32-card/64-card configuration, through faster intra-machine communication, increases inter-card interconnection bandwidth by 8 times, improves single-machine training performance by 10 times, and boosts single-card inference performance by 13 times, supporting large-scale VLA training and inference.
Baidu's Super Node Solution Based on Kunlun Chip
Image source: Baidu Cloud
Comparison of Alibaba Cloud/Huawei Super Node Architecture Servers (Partial)
Data sources: Alibaba Cloud, Huawei; Compiled by: Zuosi Auto Research
Device-Cloud Collaboration Technology Optimizes the Experience in Cockpit and Vehicle-Road-Cloud Scenarios
From 2025 to 2026, device-cloud collaboration technology, as one of the technical foundations, accelerates its penetration into the two major scenarios of cockpits and vehicle-road-cloud. With the complementary mode of "cloud computing power empowerment + vehicle-end real-time response", it solves problems such as unsmooth cockpit interaction and suboptimal performance of vehicle-road-cloud systems, optimizing user experience.
01. Cockpit Scenarios
The cockpit device-cloud collaboration architecture in 2026 achieves capability upgrades through the combined route of "cloud large model optimization + vehicle-end lightweight model execution": the cloud undertakes high-load computing and inference tasks, including complex semantic understanding, multi-turn dialogue tracking, and massive knowledge base data retrieval, which require high computing power; the vehicle end is responsible for real-time response, low-latency interaction, and privacy protection. Through technologies such as edge node sinking, the end-to-end latency is controlled within 500 milliseconds to meet user needs. Cloud vehicle machines are a typical practical application of device-cloud collaboration in cockpit scenarios.
Optimization of Local Vehicle Machine Issues by Cloud Vehicle Machines
Image source: Zuosi Auto Research
Taking the Aion Cloud Vehicle Machine released by GAC and Huawei in September 2025 as an example, this function reconstructs the cockpit computing power allocation logic through vehicle-cloud intelligent collaboration: all computing and rendering tasks are handed over to the cloud, while the local vehicle machine is only responsible for interaction and display. This reduces the local computing power consumption of the vehicle machine to only 0.02-0.03 TFLOPS, greatly reducing the computing power consumption at the vehicle end. It not only ensures a smooth experience for the new vehicle machine system but also solves the upgrade problem for older models. Without replacing hardware, even mid-to-low-end chips can perform smooth intelligent interactions.
In addition to saving computing power resources, this cloud vehicle machine also leverages the advantages of cloud resources to achieve:
Completion of cloud ecosystem aggregation, integrating over 20,000 cloud applications, and supporting the streaming of mobile applications to the vehicle machine.
Accelerating the OTA (Over-The-Air) frequency, with all application and system updates completed in the cloud. The latest version can be updated within half a day, keeping cockpit functions always in a "cutting-edge state".
GAC "Nebula Space" Application Upload Interface, Equipped in Aion UT
Image source: GAC
02. Vehicle-Road-Cloud Scenarios
In vehicle-road-cloud scenarios, the core value of device-cloud collaboration lies in opening up the data links among vehicles, roadside equipment, and cloud platforms, building a complete collaborative closed loop of "vehicle-end perception, roadside blind-spot compensation, and cloud scheduling". Among them:
The cloud undertakes core tasks such as data fusion, macro traffic flow prediction, and global scheduling optimization. Through multi-dimensional data fusion, it achieves intelligent allocation of mobility resources. The cloud control platform adopts a two-level architecture of "edge cloud + regional cloud" to achieve hierarchical processing and global optimization.
Edge computing nodes, as the hub of vehicle-road connection, ensure an end-to-end latency of ≤10 milliseconds, focusing on real-time data processing and local scheduling.
In August 2025, Dongfeng Yipai eπ007 implemented the technology to optimize the smart parking function using vehicle-road-cloud collaboration technology. The technical path is "cloud scheduling + lot-end allocation + vehicle-end execution". This technology can increase parking space utilization by 45% and increase the parking volume per unit area by 1.8 times. Relying on parking lot sensors and cloud technology, Dongfeng Yipai eπ007 requires no manual operation after entering the parking lot. The lot-end equipment can instantly complete license plate recognition, compressing the entry time to within 15 seconds.
Dongfeng Applies Vehicle-Road-Cloud Collaboration to Smart Parking Technology
Data source: Dongfeng Motor; Compiled by: Zuosi Auto Research
[Contact Information] Mobile number is the same as WeChat number
Industry Research Department | Mr. Fu 15810027571
Mr. Zhao 18702148304
Data Service Department | Ms. Zhang 13716037793
Strategic Consulting Department | Ms. Han 15810133447
Promotion and Communication Department | Ms. Liao 13718845418
Mr. Du 13910162318