--- title: "iFlytek Spark X1 and the case for fully domestic model training" date: 2026-11-06 category: Foundation Models site: NeuroAI canonical: https://neuroai.site/a/na-model-iflytek-x1-domestic-compute language: en --- # iFlytek Spark X1 and the case for fully domestic model training > iFlytek (科大讯飞) released Spark X1 (星火 X1) on 15 Jan 2025 as China's first deep-reasoning large model (大模型) trained entirely on Huawei Ascend (昇腾) 910B chips. A sovereign-compute test. Most Chinese reasoning models brag about their scores. Spark X1's (星火 X1) more unusual claim is about the factory it was built in: every training step ran on domestic chips, with no foreign accelerators in the loop. That is a harder engineering feat than it sounds, and it is the real story behind iFlytek's (科大讯飞) January 2025 release. ## Key takeaways - Spark X1 (星火 X1), from iFlytek (科大讯飞), was released on 15 January 2025 as the company's first deep-reasoning large model (大模型) with "slow thinking" capability. - iFlytek states it is the first deep-reasoning model in China trained entirely on a fully domestic computing platform, using Huawei Ascend (昇腾) 910B accelerators. - The first release was a 13B model; a 70B version followed on 3 March 2025, according to founder and chairman Liu Qingfeng (刘庆峰). - The company reports raising Huawei Ascend 910B efficiency from about 20% to nearly 80% of Nvidia-class capability through co-optimization. - Spark X1 supports a unified fast-and-slow thinking mode and can be privately deployed on as few as four Huawei 910B cards (vendor claim). ## Why "domestic" is the headline iFlytek has been on the U.S. Entity List since 2019, which blocked access to Nvidia and other restricted components. Rather than a workaround, the company turned the constraint into a design principle: train every Spark model on Huawei Ascend (昇腾) silicon. Spark X1 is the clearest expression of that line, described by iFlytek as the only general-purpose deep-reasoning model at its scale trained wholly on a domestic stack. According to Liu Qingfeng (刘庆峰), chairman of iFlytek (科大讯飞), the 13B version shipped first on 15 January 2025 because compute was constrained, and the 70B version arrived on 3 March 2025 once the domestic cluster was pushed further. The quote captures the constraint honestly: training on domestic chips costs more and takes longer, but the alternative is building "on someone else's foundation." ## The efficiency climb The bottleneck was never the model architecture; it was the chip. Liu stated that through co-optimization with Huawei, the effective efficiency of the Ascend 910B rose from roughly 20% of Nvidia-class performance to close to 80%. That is a vendor-reported figure tied to internal benchmarking, so read it as directional. But the trajectory is the point: a domestic accelerator that starts at a fifth of the efficiency and climbs toward four-fifths changes the economics of sovereign training. iFlytek built two clusters with Huawei, Feixing-1 (飞星一号) and Feixing-2 (飞星二号), the latter launched in October 2024 as an upgraded large-scale intelligent computing platform. The existence of those clusters is what made X1's fully domestic training possible in the first place. ## Reasoning design: fast and slow in one model Spark X1 is a reasoning model in the "slow thinking" sense: it decomposes hard problems, reflects, and verifies its own steps before answering. iFlytek's notable design choice is a unified model that serves both fast and slow thinking modes, switched by system instruction, rather than shipping separate models. For private deployment this matters: a hospital or school runs one image, not two. The vendor also claims parity with OpenAI's o1 and DeepSeek R1 on general reasoning. Treat that as a marketing claim, not an audited result; this article did not reproduce those comparisons. Where X1 has the clearest demonstrated strength, per iFlytek's own materials, is in Chinese-language mathematics and in education and healthcare scenarios where the company already has distribution. ## Deployment economics iFlytek states that X1 can be privately deployed on as few as four Huawei 910B cards, with support for both SFT and reinforcement-learning customization. For buyers in regulated sectors (education, healthcare, judiciary) that cannot send data off-site, a compact on-premises footprint is the actual product, more than any leaderboard score. The X1 API is also available on iFlytek's open platform for developers. A later step in the same line, Spark X1.5 (released November 2025), moved to a Mixture-of-Experts architecture of roughly 293 billion total parameters with about 30 billion active per inference, supporting 130-plus languages. iFlytek describes it as the first MoE model with full-chain training completed on a fully domestic platform. That follow-on extends, rather than replaces, the domestic-training thesis. ## Honest limitations - The "first fully domestic deep-reasoning model" and "80% of Nvidia efficiency" claims come from iFlytek and Liu Qingfeng; they are vendor-stated and not independently verified here. - The parity claim with OpenAI o1 and DeepSeek R1 is a marketing comparison, not an audited benchmark result in this article. - Efficiency of domestic accelerators depends heavily on co-optimization; results may not generalize to other labs or other chip batches. - Spark X1.5's 293B/30B MoE figures are vendor-reported; independent MLPerf-style measurements were not available at writing. ## What readers can do now - If you operate in a regulated sector that requires on-premises inference, evaluate Spark X1's four-card Huawei 910B deployment as a concrete sovereign-compute option. - Do not accept the o1/R1 parity claim at face value; run your own Chinese-language math and domain tests before deciding. - Watch the Feixing clusters' published utilization and the X1.5 MoE's active-parameter efficiency as the real signal of whether domestic-only training scales. --- Published by NeuroAI (https://neuroai.site/) — https://neuroai.site/a/na-model-iflytek-x1-domestic-compute Free to quote with attribution and a link to the original.