A question is stirring the community of those betting on locally runnable LLMs: are open weight models really viable in the long term? The issue was raised by a Reddit user looking at the recent release strategy of the Qwen team. Qwen has unveiled several new models lately, but the 122B, 35B, 27B, and 9B versions are still being held back.

A widespread hypothesis among insiders is that the team chose not to release them as open weights immediately because their performance turned out to be particularly competitive. The waiting period would serve to build a comfortable lead with the next generation before granting the current models to the public. The consequence, however, is that the gap between a model’s existence and its actual availability to the community gets wider.

Recent analyses estimate that open models already lag 2–4 months behind the most advanced proprietary systems. If Qwen alone adds 1–2 more months of waiting (or even longer), the gap risks widening in a worrying way. The comparison drawn is with what happened to Meta-Llama in the past, when a shift in release policy reshaped the open landscape. But here the crux is more specific: Qwen is the reference point for those seeking top performance on consumer-grade hardware.

Anyone working with standard GPUs, without access to clusters with hundreds of gigabytes of VRAM, wonders whether an ecosystem so dependent on third-party decisions can hold. The value of self-hosted setups – for privacy, data control, or simply to avoid the recurring costs of cloud APIs – rests on the availability of models that stay competitive. If open releases become chronically delayed, the temptation to switch to hybrid solutions or to over-squeeze smaller models via extreme quantization grows strong, though there are objective limits.

Deciding what and when to release as open weight has become a strategic lever for vendors, balancing community visibility with competitive advantage. The underlying question – whether the current model can last long-term – has no definitive answer, but the signal is clear: complete openness is increasingly negotiated, and anyone betting on local deployment must factor in a performance gap that might widen. Perhaps the real game will be played not on individual models, but on the community’s ability to bridge that gap with fine-tuning, compression techniques, and shared pipelines.