Inside China’s Machine · Tokenomics, Episode 9 of 10
The Answer Price = price per token × tokens per attempt ÷ success rate
China now has a working second supply of AI compute that hedges an import channel which has opened and closed, but no public record yet shows whether its tokens cost less or more than imported NVIDIA ones.
On 24 April 2026, DeepSeek published the prices for its new V4 models as a single image. Pro’s list prices were CNY 1 per million input tokens served from cache, CNY 12 per million input tokens not in cache, and CNY 24 per million output tokens. Tokens are the small units of text a model reads and writes. Under the numbers, the lab explained why Pro was scarce: 受限于高端算力，目前 Pro 的服务吞吐十分有限 (limited by high-end compute, Pro’s service throughput is currently very limited). Then it made a forecast that named a chip: 预计下半年昇腾 950 超节点批量上市后，Pro 的价格会大幅下调。 (Pro prices are expected to fall substantially after Ascend 950 supernodes are available in volume in the second half.)
Ascend 950 is Huawei’s next generation of AI chip. A price list that ties a future price cut to a domestic chip’s arrival is a lab’s forecast, not a measurement. It is still a useful sentence, because it states plainly what China’s own supply of compute is being asked to do: turn a scarce, expensive model into a cheap one.
Our view is this. China has built a working alternative supply of AI compute. It has hardware systems documented in dated publications with published serving numbers, a lab and a cloud operator that say their production traffic runs on domestic hardware, and, alongside that domestic supply, an import channel for NVIDIA chips that policy has opened and closed more than once. That last fact is what makes the alternative matter. What no one can show yet from public records is whether a finished token from the domestic supply costs less or more than one from the NVIDIA hardware China can import. The arithmetic that would decide the question has not been published for both sides on the same terms. The hedge is visible. A cost advantage is something nobody can yet show, and nobody can yet rule out.
The alternative is real
An accelerator is a chip built for the arithmetic that AI models run on; NVIDIA calls its accelerators GPUs, and Huawei calls its accelerators NPUs, for neural processing units. HBM, high-bandwidth memory, is memory stacked in layers right next to the chip so the model’s weights, the numerical values that encode what it has learned, can be read fast enough to keep the arithmetic busy, and some of the trade policy in this story is about HBM.
The clearest domestic system on record is CloudMatrix384, described in a Huawei-led technical paper (with SiliconFlow co-authors; version 3, 19 June 2025). It joins 384 Ascend NPU packages with 192 CPUs across 48 compute nodes in 12 racks, plus four racks for communication (16 in all). Each NPU package holds two dies, two separate pieces of silicon, so the system has 384 packages, not 768 cards. The power it consumes and the links that move data between chips belong in the system’s economics too. Like the other papers below, this is a company describing its own system, not an outside measurement.
A system that exists is not the same as one that serves paying users. On 17 September 2026, Zhipu AI (Z.ai), which makes the GLM models, wrote that GLM-5.3-Flash runs on a system of more than 100,000 Chinese-made accelerators: “All production inference for GLM-5.3-Flash runs on this system.” Inference is the work of running a trained model to answer requests. That is a company statement about its own fleet; no independent audit of the fleet is public, and the company does not name the chip.
Huawei’s 2025 roadmap puts the next generation, Ascend 950DT, in the fourth quarter of 2026. That is a roadmap date, not a delivery report; Huawei said on 17 September 2026 that Ascend 950 supernodes were on sale early. A supernode joins many accelerators into a closely connected system.
Then there is the channel that makes all this a hedge rather than a hobby. On 2 December 2024, the US Bureau of Industry and Security put export controls on high-bandwidth memory above a bandwidth-density threshold. In April 2025 the US government told NVIDIA a licence was required to export its China-market H20, and NVIDIA booked a $4.5 billion charge. On 13 January 2026, the US Commerce Department said H200-class licence applications would be reviewed on a “case-by-case basis provided certain security requirements are met.” On 26 August 2026, NVIDIA said it had shipped H200 products worth less than 1% of its total data-centre revenue to customers in China under licences that quarter, and that its forward outlook held no China data-centre compute revenue.
The pressure has come from the other side too: on 5 November 2025, Reuters, citing anonymous sources, reported guidance that state-funded data centres should not use foreign AI chips, and that projects less than 30% complete would have to remove installed foreign chips or cancel purchases. Reuters said it was unclear whether the guidance applied nationwide, and no Chinese official text of it has been found, so it remains a press report of guidance, not a published rule.
Put those records in a row and the pattern is easy to see. A Chinese buyer planning compute for 2026 could not count on any one import route staying open for a whole year. Against that, a domestic system that serves production traffic is worth something even before anyone knows its price per token, in the same way insurance is worth something before anyone knows whether the house will burn. That is the hedge, and it is the part of the case that the public record does show.