This week, a user posted on Reddit's LocalLLaMA subreddit (an English-language forum where users run open-source large models locally), releasing a 27B-parameter variant based on Alibaba's Qwen, with a blunt title: per-token generation is slower, but overall results are faster and better. We've noticed this approach — "trading inference time for answer quality," known in the trade as test-time compute — has clearly emerged for the first time on a mid-sized open-source model.
What this is
Simply put, the author fine-tunes the Qwen series so the model runs several extra internal reasoning steps before delivering its final answer. The cost is slower per-token generation; the payoff is higher accuracy and longer reasoning chains. This matches the core approach of OpenAI o1 and o3: rather than scaling up parameters at training time, the model is made to "think longer." 27B counts as a mid-sized parameter count, runnable on a single consumer GPU (such as a 4090) — a stark contrast to models with hundreds of billions of parameters.
Industry view
Supporters see this as a key inflection point for the open-source community: "slow thinking" was previously the territory of big players, since multi-step reasoning burns compute and isn't cost-effective; now someone is willing to trade speed for quality, signaling developers will pay in time for "better answers."
Opposition is equally clear. First, this is currently just an individual's community experiment, with no third-party independent replication — the claimed "better" may be overfitting on the author's test set. Second, when a 27B model runs "slow thinking," single-card VRAM gets tight, and total cost of ownership may not actually be lower than simply calling an API. Third, the gains from test-time compute have a ceiling — slow things down further and user experience collapses, with returns diminishing rapidly.
Impact on regular people
For enterprise IT: If models like this stabilize, companies may stop paying steep fees for proprietary LLM APIs — one local server could deliver answers approaching o1-level quality.
For individual professionals: Willingness to wait 30 seconds instead of 3 for an answer will become a new work habit. Tasks like coding or research that demand accuracy actually benefit from going slower.
For the consumer market: Consumer AI products (chat assistants, writing tools) won't see this shift any time soon — users are extremely latency-sensitive; unless hardware or algorithms break through again, "fast" remains the top metric for consumer products.