
DeepSeek develops custom AI chips to cut Nvidia and Huawei reliance
Published by AINave Editorial • Reviewed by Ramit
DeepSeek is developing its own custom AI chips for inference to reduce dependence on Nvidia and Huawei, according to a Reuters report. The move follows OpenAI's Jalapeño chip and signals a broader trend among AI labs to control hardware costs and supply chains.
What happened
DeepSeek has been exploring in-house AI accelerators for about a year, with discussions with chip design, foundry, and memory partners, and is actively recruiting experienced chip designers. The focus is on inference chips, which become the recurring cost center once models are deployed. DeepSeek previously trained its R1 model on Nvidia H800 chips (later banned) and its V4 model on Huawei Ascend GPUs. The company recently raised $7.4 billion in funding. Rivals Alibaba and Baidu are also developing their own AI processors.
Why AI builders should care
If successful, DeepSeek's in-house silicon could serve as a case study for controlling inference costs, a major expense for deployed models. The trend toward vertical integration of hardware and software may affect pricing and supply dynamics for AI workloads. It also illustrates how AI firms adapt to export controls and hardware constraints.
Practical implications
AI teams should monitor DeepSeek's progress as it could inform compute procurement and build-vs-buy decisions for inference. The trend may accelerate vendor diversification strategies in constrained markets.
Caveats
Plans are early-stage and subject to change. No final design, timelines, or pricing have been disclosed. Comparisons to OpenAI's Jalapeño are contextual, not confirmation of DeepSeek's product specs.
FAQs
Sources
- Report: China's DeepSeek follows OpenAI in developing its own custom inference chips - SiliconANGLE
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