Question of the Day
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How does building an inference chip—rather than a training chip—actually reduce DeepSeek’s dependence on Nvidia and Huawei?
Take-away Inference is the scalable “deployment bottleneck,” so owning inference silicon cuts supplier risk even if training stays outsourced—and it sidesteps export bans aimed at top-end training GPUs.
DeepSeek aims to reduce its dependence on Nvidia and Huawei by developing an inference chip, which is designed specifically for generating responses from pre-trained models rather than training new models [2], [4]. Currently, DeepSeek relies heavily on Nvidia and Huawei chips for both training and inference tasks. By focusing on in-house inference chip development, DeepSeek can lessen its reliance on these external suppliers for deployments of its AI models, shifting towards hardware control in its operations [1], [4]. Additionally, due to export controls and limited access to advanced Nvidia chips, DeepSeek's strategy focuses on controlling the part of the AI process (inference) that does not require the high-performance chips restricted by these export controls [5]. This strategic move aligns with reducing potential supply chain vulnerabilities and ensuring consistent hardware availability for deploying AI models [3].
- Report: China's DeepSeek follows OpenAI in developing its own custom inference chips - SiliconANGLE siliconangle.com (opens in new tab)
- DeepSeek Looks To Sidestep Huawei As It Begins In-House AI Chip Journey While Dodging Development Complications, Trade Sanctions, Delays & More wccftech.com (opens in new tab)
- Hangzhou DeepSeek developing inference chip to reduce foreign hardware reliance newsbytesapp.com (opens in new tab)
- China's DeepSeek developing its own AI chip, sources say tribune.com.pk (opens in new tab)
- Facing US export controls, China's DeepSeek plans to make its own chips - Ars Technica arstechnica.com (opens in new tab)