The Register: Dodgy Huawei chips nearly sunk DeepSeek’s next-gen R2 model

Aug 14, 2025

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Source URL: https://www.theregister.com/2025/08/14/dodgy_huawei_deepseek/
Source: The Register
Title: Dodgy Huawei chips nearly sunk DeepSeek’s next-gen R2 model

Feedly Summary: Chinese AI model dev still plans to use homegrown silicon for inferencing
Unhelpful Huawei AI chips are reportedly why Chinese model dev DeepSeek’s next-gen LLMs are taking so long.…

AI Summary and Description: Yes

Summary: The text discusses the challenges faced by Chinese AI model developer DeepSeek concerning the use of domestically produced silicon for inferencing in their next-generation language models. It highlights the impact of local hardware capabilities on AI model development timelines, shedding light on the intersection of AI and hardware security.

Detailed Description: The content addresses the ongoing development struggles of DeepSeek, a Chinese developer focused on creating large language models (LLMs). The company’s delay is attributed to difficulties with Huawei’s AI chips, which are reportedly not meeting the required performance benchmarks, emphasizing the significance of hardware in AI deployments.

Key points include:

– **Homegrown Hardware Dependency**: The reliance on domestically produced silicon raises questions about the performance and capabilities of local hardware, which can affect overall AI system efficacy.
– **Impact on AI Development**: The challenges faced by DeepSeek illustrate how hardware limitations can lead to delays in AI model deployment, prompting a reconsideration of hardware security and efficiency in AI systems.
– **Broader Implications**: This situation serves as a reminder for professionals in AI security and infrastructure security about the importance of compatible hardware in deploying effective AI solutions.

This scenario underscores the critical intersection of AI infrastructure and security, highlighting the need for robust hardware development to support AI advancements. It is particularly relevant for stakeholders concerned with the security, compliance, and performance of AI technologies reliant on specific hardware frameworks.

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