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Qwen4-27B Is Coming
Lately, news about the upcoming Qwen4 release has been circulating heavily in tech communities, with some rumors even suggesting it could launch as early as next week.
Given the solid reputation that Qwen3.8-27B built within the local model community, plus the overall architectural upgrades in Qwen4, everyone's attention for this release is focused on the smaller Qwen4-27B model.
As an early preview of the Qwen4 architecture, when Qwen3.8-Flash-Next was first released, I took a close look at its three major architectural changes: QSA, GR, and N-gram. If these are applied to Qwen4-27B, they should bring improvements in saving local memory, reducing compute requirements, and boosting model performance.
Below, I'll briefly introduce these three changes and how they might affect your local model deployment.
Quantized Sparse Attention (QSA)
The main purpose of this is to reduce the temporary memory and compute needed during inference. The word "sparse" in the name says it all—let me give a simple example.
The original fully connected layer is like someone reading a book without a table of contents—they have to read from the beginning to find information. Now this layer has a table of contents (an indexer), so it can look up the index first and then jump to the…
