Vision-language-action (VLA) models have become central to the evolution of autonomous driving. Their key advantage lies in significantly improving generalization in long-tail scenarios while enhancing system compliance through more interpretable reasoning processes, making them a critical pathway toward achieving Level 4 autonomy.

However, deploying VLA models requires high-performance hardware architectures—particularly in terms of compute density, memory capacity, and bandwidth. This report examines the hardware transformation driven by VLA and its cascading impact on controller cost structures and semiconductor supply chains.

Key Highlights

• Strategic Value: VLA models drive fully autonomous driving by boosting corner-case adaptability and reasoning transparency.
• Hardware Shift: Deploying models demands advanced hardware with high-computing chips and high-capacity, high-bandwidth memory. 
• Market Impact: This shift fundamentally reshapes controller cost dynamics and semiconductor supply chains.

Table of Contents

1. Surging Demand for Compute and Memory in the VLA Era
   • Table 1: Orin-X vs. Thor-X Dev. Kit
2. Compute, Memory, and Thermal Requirements Drive Controller Cost Increases
   • Table 2: Controller Specifications and Cost Comparison
   • Figure 1: Component Cost Breakdown across Controller Tiers
3. VLA Era: Challenges in the Chip Supply Chain and Cockpit-Driving Integration
   • Table 3: A Comparison of Automaker In-House Chips
   • Table 4: High-Performance Automotive Chips (250+ TOPS) by Vendor
4. TRI’s View

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