Reddit asks: why optimize local LLMs for coding, not world knowledge via N-gram?
ironicstatistic · reddit · 2026-09-22
A Reddit user argues that the open-source community—especially in the sub-50GB local model space—is over-indexing on making models as smart and compact as possible. Qwen3 27B excels at coding and system work, but its world knowledge lags far behind frontier models, especially at Q4 quantization.
He asks: since N-gram mechanisms introduced in Qwen3-Next offload capability to SSD/cheap storage and ease VRAM limits, why not build local models with moderate intelligence but massive world knowledge—better at screenshots, multilingual tasks, pixel art, physics questions—with training cutoffs made less relevant by swapping N-gram stores. He openly invites correction, sparking discussion on local model direction.
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