Xiaomi is pursuing vertical integration for its next generation of “smart driving”: its Xring D100 is designed to move advanced AI inference—including very large local models—into Xiaomi vehicles from 2027. The comparison is less about a single race for chip specifications than about different strategies: Xiaomi for high-end on-vehicle AI, NIO for lower supplier costs and margin improvement, XPeng for an autonomy-software/platform roadmap, and Tesla for longer-term manufacturing capacity.

• Xiaomi Xring D100: Xiaomi says the validated D100 is a 3-nm in-house intelligent-driving chip with a 20-core CPU, 16-core NPU, and up to 160 GB of unified memory. It claims the system can deploy models of up to 200 billion parameters locally and targets commercial use in 2027. Xiaomi has not publicly disclosed key comparison metrics such as TOPS, the first vehicle, or production volumes, so real-world performance and economics remain unproven. 31012

• NIO: NIO’s chip program is chiefly an economic and supply-chain strategy: reduce reliance on Nvidia, lower recurring hardware costs, and improve profitability through scale. Its Shenji NX9031 is a 5-nm intelligent-driving chip; NIO has described the broader in-house effort as an important contributor to R&D efficiency and margins. 114

• XPeng: XPeng’s Mona L03 is a nearer-term vehicle platform for its VLA-assisted-driving stack and Turing chips, while its stated Level 4 target belongs to the 2028 SEPA 4.0 roadmap, alongside an in-house AI foundation model. That is an aspiration rather than evidence that L03 itself is already a Level 4 vehicle. 1315

• Tesla/Terafab: Reuters reported that Tesla and SpaceX plan separate advanced-chip factories in Texas: one for Tesla cars and humanoid robots, and one for space-based AI data centers. Other reporting has characterized the proposed Terafab as 2-nm and says Musk indicated SpaceX could consume roughly three times Tesla’s output; those details should be treated as prospective plans, not operating capacity. 23

• HW4/AI4 caveat: Claims that current Tesla HW4/AI4 cannot enable unsupervised FSD are investor/analyst criticism, not an established technical finding. Tesla has reportedly told JPMorgan that HW4 is sufficient for FSD v15 and unsupervised operation, while also deploying AI4.5 with modestly more compute and about twice the memory. The unresolved issue is therefore validation, safety, regulation, and software capability—not merely chip-node size. 38

The important distinction: a 3-nm or 2-nm process can improve efficiency and density, but it does not itself demonstrate safe Level 4 autonomy. Xiaomi’s 200B-model claim and Tesla’s Terafab plans are strategically significant, yet both require production-scale deployment and independently observable driving performance to substantiate their autonomy implications.

Xiaomi is pursuing vertical integration for its next generation of “smart driving”: its Xring D100 is designed to move advanced AI inference—including very large local models—into Xiaomi vehicles from 2027. The comparison is less about a single race for chip specifications than about different strategies: Xiaomi for high-end on-vehicle AI, NIO for lower supplier costs and margin improvement, XPeng for an autonomy-software/platform roadmap, and Tesla for longer-term manufacturing capacity.

• Xiaomi Xring D100: Xiaomi says the validated D100 is a 3-nm in-house intelligent-driving chip with a 20-core CPU, 16-core NPU, and up to 160 GB of unified memory. It claims the system can deploy models of up to 200 billion parameters locally and targets commercial use in 2027. Xiaomi has not publicly disclosed key comparison metrics such as TOPS, the first vehicle, or production volumes, so real-world performance and economics remain unproven. 31012

• NIO: NIO’s chip program is chiefly an economic and supply-chain strategy: reduce reliance on Nvidia, lower recurring hardware costs, and improve profitability through scale. Its Shenji NX9031 is a 5-nm intelligent-driving chip; NIO has described the broader in-house effort as an important contributor to R&D efficiency and margins. 114

• XPeng: XPeng’s Mona L03 is a nearer-term vehicle platform for its VLA-assisted-driving stack and Turing chips, while its stated Level 4 target belongs to the 2028 SEPA 4.0 roadmap, alongside an in-house AI foundation model. That is an aspiration rather than evidence that L03 itself is already a Level 4 vehicle. 1315

• Tesla/Terafab: Reuters reported that Tesla and SpaceX plan separate advanced-chip factories in Texas: one for Tesla cars and humanoid robots, and one for space-based AI data centers. Other reporting has characterized the proposed Terafab as 2-nm and says Musk indicated SpaceX could consume roughly three times Tesla’s output; those details should be treated as prospective plans, not operating capacity. 23

• HW4/AI4 caveat: Claims that current Tesla HW4/AI4 cannot enable unsupervised FSD are investor/analyst criticism, not an established technical finding. Tesla has reportedly told JPMorgan that HW4 is sufficient for FSD v15 and unsupervised operation, while also deploying AI4.5 with modestly more compute and about twice the memory. The unresolved issue is therefore validation, safety, regulation, and software capability—not merely chip-node size. 38

The important distinction: a 3-nm or 2-nm process can improve efficiency and density, but it does not itself demonstrate safe Level 4 autonomy. Xiaomi’s 200B-model claim and Tesla’s Terafab plans are strategically significant, yet both require production-scale deployment and independently observable driving performance to substantiate their autonomy implications.