DeepSeek begins in-house AI chip development to cut reliance on NVIDIA
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Updates DeepSeek's case-study profile (§4) by adding custom silicon strategy; cross.§H for chip design subject; cross.§D linked to concurrent $7B fundraise explicitly in article.
DeepSeek begins in-house AI chip development to cut reliance on NVIDIA
AI startup DeepSeek has launched a confidential project to develop custom inference chips, according to a Reuters report cited by TechNode. The effort is in early stages, has been running for about a year, and focuses on reducing inference costs while lowering dependence on overseas GPU suppliers such as NVIDIA. DeepSeek has quietly recruited chip engineers across architecture, verification, and software enablement to staff the initiative. The company has not publicly commented.
The move is a textbook example of the capital-compression dynamic playing out across foundation-model labs: compute expenses now account for over half of operating costs at many AI companies, and the scarce supply of advanced GPUs has made custom silicon a strategic hedge. DeepSeek's decision to build inference-specific chips rather than training hardware aligns with the broader industry shift from model training to large-scale inference as generative AI adoption accelerates. The project will take well over a year to reach tape-out and mass production, so near-term competitive impact is limited.
DeepSeek is simultaneously pursuing its first external funding round, reportedly seeking about $7 billion at a valuation between $52 billion and $59 billion. If completed, chip development and AI infrastructure investment will become key priorities for the round. The company's open-source model DeepSeek-V3 and reasoning model R1 have driven strong user demand, making compute-cost reduction an urgent operational lever. This move joins a growing roster of AI labs — including OpenAI and Anthropic through their respective hyperscaler partners — that are designing custom inference silicon to escape single-supplier dependency.
