Automotive Memory Chip Industry and Its Impact on Foundation Models Research Report 2025: Market Set to Quadruple by 2030 Amid Rising Demand for Larger Models - ResearchAndMarkets.com

The "Research Report on Automotive Memory Chip Industry and Its Impact on Foundation Models, 2025" has been added to ResearchAndMarkets.com's offering.

The technological evolution from 2D+CNN models to BEV+Transformer foundation models has led to a skyrocketing increase in model parameters, establishing memory as a critical performance bottleneck in the automotive industry. The global automotive memory chip market is poised to grow substantially, projected to reach over USD 17 billion by 2030 from approximately USD 4.3 billion in 2023, reflecting a compelling CAGR of up to 22%. Memory chips accounted for 8.2% of the automotive semiconductor market value in 2023, a number expected to double by 2030, underscoring a significant increase in costs.

The rise in automotive memory demand is driven primarily by Large Language Models (LLMs) and foundation models, transitioning from compact 10-million-parameter CNN models to vastly larger ones with billions of parameters. As vehicles increasingly rely on complex computations, such towering model parameters have resulted in unprecedented memory demands, particularly in BEV+Transformer models where the Softmax operator's limited parallelization capabilities emphasize memory bandwidth limitations.

XPeng's focus is on innovation, unveiling its XPeng World Foundation Model, a 72-billion-parameter autonomous driving model which showcases the scaling law effect: larger models exhibit enhanced capabilities. However, the primary challenge remains the bottleneck in data access efficiency rather than just GPU availability. XPeng is enhancing its data infrastructure, citing a 22-fold increase in data upload capacities and 15-fold improvement in bandwidth, accelerating model training speeds fivefold using vast quantities of video clip data.

XPeng's push involves deploying its expansive "XPeng World Foundation Model" via cloud distillation to effectively leverage small models in on-vehicle implementation, necessitating advanced computational solutions. The company has engineered its Turing AI chip, improving utilization by 20% over standard chips, facilitating models up to 30 billion parameters-well beyond competitors like Li Auto's 2.2 billion-parameter Vision-Language Model (VLM).

Memory bandwidth, critical to determining inference speed, remains a constraint. Although current LPDDR5X adoption is widespread, its limitations push the agenda towards advanced technologies like GDDR7 and HBM. Tesla's early FSD chip memory bandwidth meets only 7 billion parameter models at suboptimal frame rates, while Nvidia's ORIN offers higher bandwidth but still falls short after factoring actual computation time.

The automotive industry is on the verge of transitioning from LPDDR5X to LPDDR6, aimed at significantly improving data rates and energy efficiency, although its mass production will commence post-2026. Hybrid architectures, such as combining GDDR7 for intensive AI processing with LPDDR5X for regular computing, could reconcile performance with cost-effectiveness.

Another memory frontier, High Bandwidth Memory (HBM), dominates server applications but is costly for automotive deployment. Companies like SK Hynix and Samsung are developing on-device HBMs to offer moderate bandwidth with low power for edge devices. Technological innovations in packaging, like Samsung's LPW DRAM, suggest a potential leap in bandwidth lessening power consumption.

UFS 3.1 storage solutions, prevalent in autonomous vehicles, shall advance to UFS 4.0 and UFS 5.0, though their speeds lag behind PCIe NVMe SSDs. The latter offers high-speed data transfer essential for L3/L4 autonomous vehicles, facilitating efficient AI computing and real-time processing capabilities-reinforced by Synopsys's introduction of a PCIe 5.0 automotive-grade IP solution that meets critical standards, indicating its imminent integration into automotive applications.

Key Topics Covered:

Overview of Automotive Memory Chip Industry

• Classification of Automotive Memory Chips
• Demand Characteristics of Automotive Memory Chips
• Global Memory Chip Market and Development Prospects for Automotive Memory
• Application Trends of Automotive Memory Chips

Development Trends of Automotive Memory Chips in Various Application Scenarios

• Memory Demand Under the Evolution Trend of Autonomous Driving
• AI Empowers the Automotive Sector, and Increases Memory Demand
• Development Trends of Autonomous Driving Systems
• Challenges to Automotive Memory in the Era of Foundation Models
• Memory Demand Under the Trend of Edge AI Deployment in Cockpit
• Memory Demand of Central Supercomputing Under EEA Evolution
• Memory Demand Under the Trend of Automotive Data Recording Compliance
• Summary of Automotive Memory Application Trends

Production, Testing, Certification, and Localization Progress of Automotive Memory Chips

• Classification of Automotive Memory Chip Vendors
• Automotive Memory Chip Industry Chain and Market Pattern
• Manufacturing and Packaging & Testing of Automotive Memory Chips
• Capacity Layout of Automotive Memory Chip Wafer Manufacturers
• Certification Standard System for Automotive Chips
• Localization Level and Progress of Automotive Memory Chips

Technology Trends of Automotive Memory Chips by Product Segment

• Application Trends of Automotive DRAM
• Application Trends of Automotive Flash Memory
• Automotive Memory Trends: In-Memory Computing

Automotive Memory Chip Wafer Manufacturers

• CXMT
• YMTC
• Samsung
• SK Hynix
• Micron
• KIOXIA (Toshiba)

Automotive Memory Chip Product Manufacturers

• GigaDevice
• Ingenic
• Xi'an UniIC
• Montage Technology
• Longsys

Companies Featured

• CXMT
• YMTC
• Samsung
• SK Hynix
• Micron
• KIOXIA (Toshiba)
• Western Digital
• Silicon Motion
• Fujitsu
• Neo Semiconductor
• Nanya Technology
• GigaDevice
• Ingenic
• Xi'an UniIC
• Montage Technology
• Longsys
• XMC
• Giantec Semiconductor
• Pramor Semiconductor
• Fudan Microelectronics
• Macronix
• Biwin Storage
• Winbond
• SanDisk
• YEESTOR
• Dosilicon
• KXW
• JingCun Technology
• Phison
• Belling
• Gencun Technology

For more information about this report visit https://www.researchandmarkets.com/r/ph7tjr

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