By Chinese automakers are pouring substantial investments into artificial intelligence (AI) and computing power, aiming to rival Tesla’s leadership in the autonomous driving sector. Companies like Li Auto, Huawei, and Xpeng Motors are at the forefront of this technological race, striving to achieve end-to-end (E2E) intelligent driving capabilities that could redefine the future of transportation.
The GPU Gold Rush
Since mid-2024, there’s been a frantic scramble for GPU resources within China’s automotive industry. An insider revealed, “Since the latter half of this year, Li Auto has nearly bought out its dealers’ entire stock of GPU cards.” This rush, initially sparked by startups working with large language models, has now engulfed major players in the automotive sector.
- Autonomous Ambitions: Companies are racing to develop fully autonomous vehicles.
- Data Demands: E2E systems require immense computing power to process billions of parameters.
- Competitive Edge: Securing top-tier GPUs is crucial for staying ahead in the AI race.
This competition for GPUs, especially Nvidia’s H100 and A800 models, has intensified due to US export restrictions. The A800, Nvidia’s downgraded model, has become the most accessible option for Chinese firms. Each A800 card delivers 320 teraflops at FP16 precision, but achieving one exaflop demands around 3,125 A800 cards, costing approximately USD 133,000 per server.
Massive Investments in Computing Power
Li Auto is leading the charge, having already stockpiled thousands of high-performance chips and scouting new locations for data centers. “Li Xiang often asks me if we have enough computational power, and if not, to get more,” said Lang Xianpeng, head of intelligent driving at Li Auto.
Investment Highlights:
- July 2024: Cloud computing capacity reached 2.4 exaflops.
- August 2024: Surged to 5.39 exaflops, nearly tripling in under a month.
- Future Goals: Xpeng Motors aims to expand from 2.51 to 10 exaflops by 2025, while Huawei boosts its capacity from 5 to 7.5 exaflops in just two months.
These aggressive investments are necessary as automakers strive to develop sophisticated autonomous systems that can handle the complexities of real-world driving.
The Tesla Benchmark
Tesla remains the industry benchmark with an estimated 67.5 exaflops of AI computing power, equivalent to roughly 67,500 Nvidia H100 GPUs. Over the past year, Tesla’s GPU resources have increased sixfold, establishing a formidable lead.
Tesla’s Achievements:
- Full Self-Driving (FSD) V12: Powered by immense data and computing capacity.
- Data Collection: Millions of cars globally contribute to a vast dataset.
- Market Leadership: Significant portion of last year’s global total of 910 exaflops.
Tesla’s advanced FSD model offers a smoother, more human-like driving experience, setting high standards that Chinese automakers are eager to meet or surpass.
The Data Dilemma
E2E autonomous driving hinges on the synergy between vast data and powerful computing resources. Tesla’s approach involves collecting over a million high-quality, diverse video clips, significantly enhancing system performance.
Challenges:
- Data Collection Costs: Gathering high-quality data is expensive and time-consuming.
- Limited Useful Data: Only a small fraction of driven miles yield valuable training data.
- Data Processing: Extracting meaningful insights from vast datasets requires sophisticated algorithms and significant computational power.
Data Strategies:
- Production Car Mining: Engineers set rules for data collection from active vehicles.
- Skilled Driver Data Gathering: Employing drivers to collect high-quality training material.
Despite the high costs, these strategies are essential for building robust autonomous systems that can learn and adapt to various driving scenarios.
Financial Implications of AI Investments
The financial stakes are high as automakers invest billions to secure the necessary computing power. For instance, securing one exaflop of computing power requires an investment of approximately USD 518 million, translating to over USD 140 million spent by Li Auto in just the past month.
Cost Breakdown:
- A800 Cards: USD 133,000 per server with eight cards.
- Exaflop Investment: USD 3.7 billion per exaflop.
- Recent Spending: Li Auto has invested over USD 140 million on GPU chips alone.
These investments are critical for automakers to develop and deploy E2E autonomous systems, driving the next wave of innovation in the automotive industry.
Table: Comparative AI Computing Investments
| Company | Current Cloud Capacity | Target Cloud Capacity (2025) | Investment (USD Billion) |
|---|---|---|---|
| Li Auto | 5.39 exaflops | 10 exaflops | 1.4 |
| Xpeng Motors | 2.51 exaflops | 10 exaflops | 3.7 |
| Huawei | 7.5 exaflops | 7.5 exaflops | – |
| Tesla | 67.5 exaflops | 67.5 exaflops | – |
This table highlights the scale of investments being made by leading Chinese automakers to compete with Tesla’s AI prowess.
Autonomous Systems on the Horizon
E2E autonomous systems are nearing profitability, with Tesla already rolling out its E2E FSD and planning to launch it in China by Q1 2025. This move is expected to open new revenue streams and enhance market penetration.
Profitability Indicators:
- Subscription Models: Tesla reduced its FSD subscription fee to boost adoption.
- Market Expansion: Plans to introduce FSD in key markets like China.
- Commercialization: Moving towards mass production and deployment of autonomous systems.
These steps indicate that E2E technology is transitioning from development to full commercialization, setting the stage for widespread adoption.
The Road Ahead
Chinese automakers face significant challenges but remain undeterred in their quest to achieve autonomous driving excellence. Continued investments in AI and data, coupled with strategic partnerships and innovations, will be crucial in determining who ultimately leads the autonomous driving revolution.
Future Focus Areas:
- Advanced AI Algorithms: Developing more sophisticated models for better decision-making.
- Enhanced Data Collection: Expanding data sources to improve system accuracy.
- Infrastructure Development: Building robust data centers to support growing computing needs.
As the race intensifies, the outcome will shape the future of autonomous driving, with profound implications for the global automotive industry.