DeepSeek Turns Back to Nvidia for R2 Model After Huawei AI Chip Setback

 

DeepSeek’s R2 AI Model Stumbles: Why Huawei’s Chips Couldn’t Deliver and Nvidia Came to the Rescue

Artificial Intelligence is one of the hottest topics in technology today. Nations are pouring billions into AI research, companies are racing to develop the most powerful models, and the competition between countries to dominate the field is intense. In China, the push to create world-class AI technology — without relying on foreign suppliers — has been at the heart of its tech strategy.

But as one of China’s most promising AI companies, DeepSeek, has discovered, the road to technological independence is not as smooth as official narratives suggest. What looked like a bold leap forward has instead turned into a cautionary tale about ambition, limitations, and the realities of cutting-edge hardware.

A Quick Recap: Who is DeepSeek?

DeepSeek shot into the spotlight in January 2025 when it launched R1, its first large AI model. The launch was met with excitement both in China and internationally. The model was powerful, versatile, and positioned as a domestic competitor to models from OpenAI and Anthropic.

Its success was symbolic — proof that China could develop AI systems capable of challenging Western dominance in the field. Almost immediately after R1’s launch, expectations soared for the company’s next big release: the R2 model.

The Push to Go All-Chinese

After R1’s debut, DeepSeek came under significant pressure from Chinese authorities and the wider tech ecosystem to use homegrown hardware for future development. The logic was straightforward: if China could build both world-class AI models and the chips to run them, it would be far less vulnerable to U.S. export restrictions and sanctions.

According to multiple sources familiar with the situation, DeepSeek was given a clear message:

“Use Huawei’s Ascend AI chips instead of Nvidia’s.”

Huawei, China’s largest tech giant, has been working for years to produce chips that can rival Nvidia’s market-leading GPUs. The Ascend series is the company’s flagship line for AI workloads, and showcasing them in a high-profile project like R2 would have been a major PR win.

The Reality Check

The plan sounded good on paper. But when DeepSeek’s engineers actually began training the R2 model using Huawei’s chips, they ran into persistent technical problems. These were not minor glitches — they were fundamental performance and stability issues that stopped the project in its tracks.

Training an advanced AI model is not the same as running one. This is where the analogy often used in the AI world comes in:

• Training is like sending a student to university for years of study — long, demanding, and resource-intensive.

• Inference (running the trained model) is like asking the graduate a question — much faster and less demanding.

Huawei’s chips, the engineers discovered, might be able to handle the easier inference stage, but they struggled with the intense demands of training.

Despite help from Huawei’s own engineering team — who reportedly spent time at DeepSeek’s offices trying to solve the issues — the training runs simply could not be completed successfully.

One insider summarized the situation bluntly:

“The model’s May launch was scrapped because the chips just couldn’t handle the job.”

The Nvidia Retreat

With deadlines looming and no workable solution in sight, DeepSeek had little choice but to switch back to Nvidia’s industry-leading hardware. This was not the outcome Beijing had hoped for, but in the high-stakes world of AI development, falling behind can mean losing relevance entirely.

Nvidia’s GPUs, like the A100 and H100, have become the gold standard for training massive AI models. They’re powerful, mature, and supported by a robust software ecosystem that makes it easier for developers to get results. The problem, of course, is that U.S. export controls make it extremely difficult for Chinese companies to buy them in large quantities — which is precisely why China wants to develop its own alternatives.

Why This Matters

To understand the significance of DeepSeek’s setback, you have to see the bigger picture. AI development is as much about hardware as it is about algorithms. Without powerful chips, even the smartest AI code can’t scale to cutting-edge levels.

In recent years, the U.S. has tightened its control over the export of high-performance chips to China, specifically targeting Nvidia’s AI-focused GPUs. This has created a strategic bottleneck for Chinese companies, forcing them to accelerate efforts to create domestic alternatives.

The problem is that catching up in chip design and manufacturing is extremely hard. As Huawei’s CEO Ren Zhengfei admitted earlier this year:

“The U.S. has exaggerated Huawei’s achievements. We are not that great yet. Our best chips are still a generation behind.”

That “generation gap” can make the difference between a smooth training run and a total failure.

A Look Inside AI Training Challenges

Training modern AI models like DeepSeek’s R2 involves staggering computational requirements:

• Data Volume: Billions or even trillions of words, images, or other data types.

• Parallel Processing: Thousands of chips working together in perfect synchronization.

• Memory Bandwidth: Moving huge amounts of data between memory and processors without bottlenecks.

• Stability: Training can take weeks or months; even a small error can waste enormous resources.

If a chip can’t deliver consistently across all these areas, the whole process can collapse. This is why Nvidia’s dominance has been so hard to shake — it’s not just about raw performance, but reliability, developer tools, and years of refinement.

The Political Angle

In China, technology and politics are tightly intertwined. AI is not just a commercial product; it’s part of a national strategy for economic growth and geopolitical influence. When a company like DeepSeek succeeds, it’s seen as a win for the country. When it stumbles, the consequences are more than just financial.

Beijing’s vision of “self-sufficiency” in technology is a response to real vulnerabilities. The U.S. has shown it can choke off supply of critical components, and AI chips are among the most strategically important. But DeepSeek’s experience shows that building a complete domestic supply chain — from chip design to AI training — will take more time and investment.

What Happens Next for DeepSeek

DeepSeek is not giving up on Huawei’s chips entirely. According to insiders, the company still hopes to use them for the inference stage of the R2 model once training is completed on Nvidia hardware. This would still showcase Huawei’s capabilities, at least for less demanding tasks, and keep the door open for future improvements.

Meanwhile, the switch back to Nvidia means DeepSeek can continue developing without losing too much ground to competitors. But the delay — and the public knowledge of the difficulties — is a reminder that even the most promising tech companies face hard limits.

The Broader Lessons

This episode offers a few key takeaways for anyone watching the AI race:

1. Hardware is King — The best AI algorithms in the world are useless without chips that can run them at scale.

2. Self-Reliance Takes Time — Ambitions for technological independence can’t override the engineering realities.

3. PR vs. Reality — Official narratives often paint a picture of unstoppable progress, but real-world innovation is messy, unpredictable, and full of setbacks.

4. Global Interdependence Remains — Even as countries talk about “decoupling,” the truth is that cutting-edge technology often relies on a global web of suppliers, expertise, and tools.

Conclusion: A Setback, Not a Defeat

DeepSeek’s struggle with Huawei’s chips is a setback, but not a death sentence for either company. In the fast-moving world of AI, hardware and software evolve rapidly, and today’s limitations could be tomorrow’s breakthroughs.

For now, though, Nvidia remains the undisputed leader in AI training hardware, and China’s quest for a fully self-sufficient AI ecosystem is still a work in progress.

If anything, this episode might push Huawei and other Chinese chipmakers to double down on improving their products. For DeepSeek, the focus will be on delivering R2 successfully, even if it means leaning on foreign technology for a little longer.

And for the rest of us watching, it’s a reminder that in the race for AI dominance, the finish line is always moving — and getting there takes more than just ambition.