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Models2026-08-27 · source-backed
A 217-upvote writeup breaks down Qwen4Exp: a router picks experts late in the layer with a large payload, so experts resist offload, while an n-gram table is a hash lookup known early and cheap to fetch. The author concludes roughly 25% of weights can move to n-gram before the tradeoff inverts, turning a 176B model into 125B in RAM plus 51B elsewhere (r/LocalLLaMA). The thread's correction to the original SSD claim is the operational detail: the n-gram index is hashed, so access is random and RAM is the only tier that works.
Each link below shares sources, entities, or timing with this story.
Shared entities / Earlier coverage
Both cover LocalLLaMA, Qwen4, SSD; earlier LocalLLaMA coverage from 2026-08-26.
Shared entities / Shared topic / Earlier coverage
Both cover RAM, SSD; overlapping topics (architecture, expert); earlier RAM coverage from 2026-08-04.
Shared entities / Same source domain / Earlier coverage
Both cover LocalLLaMA, RAM; reported by the same outlet (reddit.com); earlier LocalLLaMA coverage from 2026-08-03.
Both cover LocalLLaMA, RAM; reported by the same outlet (reddit.com); earlier LocalLLaMA coverage from 2026-08-02.
Shared entities / Shared topic / Earlier coverage
Both cover RAM, SSD; overlapping topics (author, expert); earlier RAM coverage from 2026-07-30.
Shared entities / Same source domain / Earlier coverage
Both cover LocalLLaMA, RAM; reported by the same outlet (reddit.com); earlier LocalLLaMA coverage from 2026-04-11.
Shared entity: LocalLLaMA / Same source domain / Shared topic / Earlier coverage
Both cover LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (author, claim).
Both cover LocalLLaMA; reported by the same outlet (reddit.com); overlapping topics (expert, only).