Why Deepseek Delayed the R2 Model? Latest Information

Hey there, fellow AI enthusiast! If you’re like me, you’ve been eagerly awaiting the release of DeepSeek’s next-generation model, R2. Initially scheduled for a May 2025 launch, it’s now August, and we’re still waiting. So, what’s the holdup? Let’s discuss the nitty-gritty of why deepseek delayed the R2 model:

DeepSeek, the Chinese AI company that wowed us with its R1 model, initially trained it using Nvidia GPUs. However, due to U.S. export restrictions on high-end Nvidia chips, DeepSeek faced GPU shortages. In response, Chinese authorities urged the company to switch to Huawei’s Ascend processors for the R2 model. Unfortunately, these chips encountered significant issues, leading DeepSeek to revert to Nvidia GPUs for training. Additionally, extended data-labeling timelines have further slowed development.

So, the delay boils down to three main factors that give us a glance at why DeepSeek delayed the R2 model:

1. Training Failures on Huawei’s Ascend Chips
2. Forced Fallback to Nvidia Hardware
3. Data Labeling Delays and Tightening Competition

Why Huawei’s Ascend Chips?

Why did DeepSeek even consider Huawei’s Ascend chips? Let’s break it down:

Geopolitical Pressure and U.S. Sanctions

U.S. sanctions prohibited Nvidia from exporting high-end AI GPUs (e.g., H100, H20) to China. In response, Chinese authorities directed domestic AI firms to reduce reliance on U.S. technology. DeepSeek was explicitly urged to switch to Huawei’s Ascend chips to align with national self-sufficiency goals. Even after the U.S.-China agreement allowed limited Nvidia H20 sales, Chinese regulators required companies to justify orders of Nvidia chips and prioritize domestic alternatives like Huawei Ascend or Cambricon. This pressure led DeepSeek to adopt Huawei hardware despite technical challenges.

Supply Chain Security and Chip Shortages

U.S.A. sanctions caused severe shortages of high-end Nvidia GPUs in China, disrupting AI development timelines. DeepSeek’s R2 training, initially planned for May 2025, faced delays partly due to insufficient Nvidia hardware. While Huawei’s Ascend chips (e.g., Ascend 910B) were more readily available in China, they made them a pragmatic alternative amid supply constraints.

National Ecosystem Development

Chinese authorities aimed to build a competitive domestic AI stack by forcing companies to adopt Huawei’s CANN software toolkit and Ascend GPUs. This would reduce long-term dependence on Nvidia’s CUDA ecosystem. Though Ascend chips failed in training, DeepSeek used them for inference to ensure compatibility for Chinese clients (e.g., Tencent, Baidu) who operate on Huawei infrastructure.

Strategic Alignment with Policy Directives

The Cyberspace Administration of China (CAC) summoned tech firms, including DeepSeek, to justify Nvidia chip purchases and accelerate the adoption of domestic alternatives. They believed that successfully training R2 on Ascend would demonstrate China’s technological independence, countering U.S. dominance in AI hardware.

Why DeepSeek R2 Reverted to Nvidia GPUs?

After attempting to train R2 on Huawei’s Ascend chips, DeepSeek faced several challenges. It reverted to Nvidia due to the following reasons:

Technical Failures During Training

Huawei’s Ascend chips (e.g., Ascend 910B/910C) suffered from persistent technical issues during training, including unstable computing clusters, slower chip-to-chip interconnects, and limitations in Huawei’s CANN software toolkit. These flaws disrupted large-scale training runs essential for advanced AI models. Despite deploying engineers to DeepSeek’s data centers, not a single successful training run was achieved on Ascend hardware.

Launch Delays & Competitive Pressure

DeepSeek R2 was planned to be launched in May 2025, but was scrapped due to Ascend-related setbacks, giving rivals (e.g., Alibaba’s Qwen3) time to gain ground. DeepSeek’s CEO, Liang Wenfeng, expressed frustration with R2’s progress, pushing the team to prioritize performance over political compliance.

Nvidia’s Ecosystem Advantage

Huawei lacks a mature equivalent to Nvidia’s CUDA software stack, which optimizes AI workflows. As one report noted:

“Without CUDA, even the most powerful chip remains a nicely packaged problem.”

Moreover, Nvidia’s hardware-software integration ensures stability for large-scale training—something Ascend couldn’t match despite claims of superior specs (e.g., memory bandwidth).

Why Was Data Labeling for DeepSeek R2 Delayed?

To understand why DeepSeek delayed the R2 model, it’s important to know that data labeling is a crucial step in training AI models. For DeepSeek’s R2 model, this process faced several challenges:

Complexity of Updated Model Capabilities

The R2 model introduced more advanced features and capabilities compared to its predecessor, R1. This necessitated a more detailed and nuanced labeling process to ensure the model could learn effectively from the data. As a result, the labeling process took longer than initially anticipated.

Resource Constraints

The shift from Nvidia to Huawei’s Ascend chips for training led to technical setbacks, as previously discussed. These issues diverted resources and attention away from the data labeling process, which resulted in further extending the timeline.

Quality Assurance

Ensuring the accuracy and quality of labeled data is paramount. Given the advanced nature of the R2 model, DeepSeek likely implemented more rigorous quality assurance measures, which, while beneficial, contributed to the delays.

• You can also download DeepSeek R1 APK from this website.

How Long Did the Data Labeling Take?

While specific timelines aren’t publicly disclosed, industry standards suggest that data labeling for large-scale AI models can span several months. Considering the added complexities of the R2 model, it’s reasonable to infer that the process extended beyond initial projections.

Why Deepseek Delayed the R2 Model: Conclusion

So, here we are—still waiting for DeepSeek’s R2 model to make its grand debut. If you’re feeling a bit like you’re stuck in a traffic jam with no snacks, you’re not alone. But before we start throwing tomatoes, let’s take a step back and appreciate the journey.

DeepSeek’s R2 model has faced its fair share of hurdles. From grappling with Huawei’s Ascend chips to navigating data labeling delays, it’s been a rollercoaster. But hey, every great story has its challenges, right?

Despite the setbacks, DeepSeek is pushing forward. They’re learning, adapting, and striving to deliver a product that meets high standards. It’s like watching a chef perfecting a new recipe—sometimes, it takes a few tries to get it just right.

While the wait continues, it’s important to remember that innovation isn’t always a straight path. DeepSeek’s journey with R2 is a testament to the complexities of AI development and the dedication required to overcome obstacles.

So, let’s hang in there. The best things often come to those who wait—and maybe with a little humor along the way. Keep your eyes peeled; R2 might be around the corner.