2026 年趨勢：VLA 時代運算能力與記憶體領域的競爭

視覺-語言-動作（VLA）模型是自動駕駛演進的核心。它們的主要優勢在於顯著提升了長尾場景下的泛化性能，同時透過更易於解釋的推理過程增強了系統的一致性，使其成為實現L4級自動駕駛的關鍵一步。

然而，VLA模型的實作需要高效能的硬體架構，尤其是在運算密度、記憶體容量和頻寬方面。本報告檢驗了VLA驅動的硬體變革及其對控制器成本結構和半導體供應鏈的連鎖影響。

主要亮點

• 策略價值：VLA 模型透過增強對特殊情況的適應性和推理的透明度，推動完全自動駕駛。
• 硬體改造：此模型的引入需要配備高性能晶片和大容量、高頻寬記憶體的先進硬體。
• 市場影響：這項變革將從根本上重組控制器和半導體供應鏈的成本結構。

目錄

第1章：超大規模積體電路時代計算與記憶體需求的激增

第2章：計算、記憶體和散熱需求推高控制器成本

第3章：VLA時代：晶片供應鍊與駕駛座整合面臨的挑戰

第4章：TRI的觀點

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