This week, a post on r/LocalLLaMA racked up over 500 replies — and the topic wasn't which new model is strongest, but "why won't labs release the models we want?" User demand for Qwen 35B and 122B (two unreleased Qwen versions with roughly 35 billion and 122 billion parameters respectively) has persisted for months, but Alibaba hasn't budged. What we want to know: who actually decides the production schedule for open-source large models?
What This Is
Qwen is Alibaba DAMO Academy's open-source large model series, and over the past two years has been one of the most active publishers in China's open-source camp. But users have been waiting for two sizes: 35B and 122B — sitting between the already-released 72B and 235B. These two sizes are considered the "sweet spot" for enterprise on-premise deployment (running the model on the company's own servers): stronger than 72B, yet more compute-efficient than 235B.
The real question Reddit users are debating: why doesn't open-source community demand translate directly into lab output? The answer is — lab priorities aren't driven by user demand alone. Compute budgets, differentiation from closed-source versions, commercialization roadmaps, and recruiting signals all make up the production schedule. User pressure helps, but it's just one lever, not the whole machine.
Industry View
Supporters argue this actually reflects the health of open-source AI. Qwen has released dozens of versions cumulatively, making it one of the most active open-source forces globally — and "pressuring for updates" is itself users' endorsement of Alibaba's roadmap. One long-time Qwen developer said: "Compared to Mistral and Meta, Alibaba's responsiveness to community feedback is already top-tier."
But the dissent is equally sharp. One developer wrote: "We thought open source was collaboration; turns out it's free market research for big companies." This phenomenon of "users shouting themselves hoarse while labs stay unmoved" exposes a structural contradiction in open-source AI: users contribute feedback, testing, and word-of-mouth, but labs aren't actually accountable to the community.
The more realistic read: the release cadence of open-source models has never been about "what users want," but "what the lab chooses to ship under compute, brand, and regulatory constraints." The reason Qwen 35B/122B haven't shipped may not be unwillingness — it could be that more urgent items are higher on the production schedule — such as the next-gen flagship, or a compliance-tailored variant.
Impact on Regular People
For enterprise IT: Don't bet selection on "soon-to-be-released" models. As long as Qwen 35B isn't shipped, plans need a fallback based on already-released versions. The compute budget for on-premise deployment should be calculated against 72B, not 35B.
For individual careers: The real value of the open-source ecosystem comes from "what you can run," not "what you're waiting for." Deploying existing Qwen versions on-premise remains one of the most worthwhile skills to learn this year. Instead of waiting for 35B, get 72B tuned and running first.
For the consumer market: User demand ≠ vendor commitment. Between an AI product's "coming soon" and "ready to use" lies an entire compute allocation table. Instead of buying based on keynote hype, check what's actually runnable on GitHub.