At the Yunqi Conference on September 22, Alibaba CEO Wu Yongming pushed the Qwen model family into a parameter range no one has publicly stated before: the flagship Qwen 3.8 Max already sits at 2.4 trillion parameters, Qwen 4 is in training and will keep climbing, and the further-out Qwen 4.5 and Qwen 5 series are targeted at 5 to 10 trillion parameters. To carry that scale, Alibaba is upgrading its inference silicon in lockstep: the in-house Zhenwu V900 triples the performance of the previous M890 node, can scale a single cluster up to 500,000 cards, and is slated for volume production in Q1 2027.

Scale Is Not a Simple "Bigger Is Better"

Pushing Qwen 4.5 and Qwen 5 to the 5 to 10 trillion range maps directly to the goal Wu keeps repeating: completing "more complex, long-horizon tasks" on the path to artificial superintelligence (ASI). This is not a "we shipped a bigger model" press release — it is a roadmap in which "a larger base model → more stable long-chain task execution" is the explicit thesis. Qwen 3.8 Max is already a sparse-MoE flagship at 2.4 trillion parameters; Qwen 4 expands on that, and 4.5 / 5 doubles into the 5–10 trillion band. The hidden cost is inference compute: M890 nodes can host 2-trillion+ models, but to serve 5–10 trillion parameters you basically have to swap out the hardware platform — which is exactly why Zhenwu V900 is being unveiled alongside the new parameter targets.

Zhenwu V900: The Inference Floor Built to Run the Bigger Model

V900 is sold as "cluster, not single-chip": a single cluster scales to up to 500,000 cards, performance is three times M890, and the target node carries 144GB of HBM (the spec shows up in Baidu Baike's entry, though Alibaba has not published full official specs yet). Wu positioned V900 as the inference substrate for the 5–10 trillion model line — the basic idea being "widen the road first, then drive the bigger vehicle." The Q1 2027 production target means V900 is being laid down in parallel with Qwen 4.5 / Qwen 5 training and inference, not as a simple M890 successor.

Strategic Intent: Closing the "Train – Infer – Deploy" Loop

From the public statements, Alibaba's stack is drifting hard toward in-house: base model (Qwen 3.8 Max → Qwen 5), inference silicon (M890 → V900), cloud capacity (Alibaba Cloud data center expansion), and product surfaces (DingTalk, Taobao, Amap). Yunqi also disclosed that Alibaba Cloud will expand data center capacity in lockstep, which welds "model – chip – data center" into a single line. In other words, V900 is not just an inference accelerator; it is a specific, physical manifestation of Alibaba's attempt to keep the entire AI stack in its own hands.

Reality Check: What Does "5–10 Trillion Parameters" Actually Mean?

For reference, a few public numbers: Mozilla's "State of Open Source AI" report puts the gap between US frontier closed-source models and the best Chinese open-weight models at roughly 4.4 months; the prior-generation Qwen3-Max was pretrained on 36T tokens, and Qwen 3.8 Max is 2.4 trillion parameters. Going to 5–10 trillion is another 2–4× jump, and on a MoE architecture that implies the active-parameter budget has to be redesigned alongside the total. Closed-source frontier models from OpenAI and Anthropic still publicly sit in the low-trillion band, so Alibaba's putting a concrete "5–10 trillion" target on the roadmap for its next one or two generations — a quantitatively explicit commitment that few players in the industry have been willing to write down.

So What?

For the domestic AI scene, the Qwen roadmap gives a concrete "how big can we go in two years" target. For the hardware scene, V900 pushes "inference accelerator" into the half-million-card cluster scale. For the industry, it means Alibaba is bundling training, inference, and data-center capacity into a single in-house stack, going deeper on the open-weight + custom-silicon axis. In the short term, Qwen 4 still lives in the 1.x-trillion band; Qwen 4.5 / 5 is where the "5–10 trillion" target actually lands — and V900's Q1 2027 production cadence is basically the timeline for that whole line.